{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Chapter 4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jo-cho/advanced_in_financial_machine_learning/blob/master/Chapter_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-uSzxrsUYZs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4mzVeH2Uc5y",
        "colab_type": "text"
      },
      "source": [
        "# Chapter 4 Exercise"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CdcGIjGUUhIG",
        "colab_type": "code",
        "outputId": "a5d1d0a1-9c71-421b-b485-1474f064f41e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "!pip install -q mlfinlab\n",
        "import mlfinlab\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns; sns.set()\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[?25l\r\u001b[K     |██▊                             | 10kB 20.5MB/s eta 0:00:01\r\u001b[K     |█████▌                          | 20kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 30kB 4.8MB/s eta 0:00:01\r\u001b[K     |███████████                     | 40kB 3.1MB/s eta 0:00:01\r\u001b[K     |█████████████▉                  | 51kB 3.8MB/s eta 0:00:01\r\u001b[K     |████████████████▋               | 61kB 4.5MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 71kB 5.2MB/s eta 0:00:01\r\u001b[K     |██████████████████████▏         | 81kB 5.9MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 92kB 6.5MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▊    | 102kB 5.1MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 112kB 5.1MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 122kB 5.1MB/s \n",
            "\u001b[?25hGo to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNxwxAK_UyMO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlfinlab import data_structures, features, filters, labeling"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A9LgnV6qUkb0",
        "colab_type": "text"
      },
      "source": [
        "## 4.1."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M2oJnrkEVUD3",
        "colab_type": "text"
      },
      "source": [
        "## (a) t1(vertical barrier touch 시간) 뽑아내기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-Jr93c0VHzb",
        "colab_type": "code",
        "outputId": "6c938a61-be5c-4a74-a75e-d18171dbef6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        }
      },
      "source": [
        "#달러 바 만들기\n",
        "raw_dollar_bars = data_structures.get_dollar_bars('/content/drive/My Drive/Colab Notebooks/csv/clean_IVE_tickbidask2.csv', threshold=1000000)\n",
        "#threshold = 1백만\n",
        "\n",
        "#df 다듬기\n",
        "dollar_bars = raw_dollar_bars.set_index(pd.to_datetime(raw_dollar_bars.date_time))\n",
        "dollar_bars = dollar_bars.drop(columns='date_time')\n",
        "dollar_bars.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Reading data in batches:\n",
            "Batch number: 0\n",
            "Returning bars \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:46:35</th>\n",
              "      <td>50.79</td>\n",
              "      <td>51.07</td>\n",
              "      <td>50.710</td>\n",
              "      <td>51.07</td>\n",
              "      <td>19844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:53:49</th>\n",
              "      <td>51.07</td>\n",
              "      <td>51.15</td>\n",
              "      <td>51.050</td>\n",
              "      <td>51.14</td>\n",
              "      <td>20324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 09:55:26</th>\n",
              "      <td>51.14</td>\n",
              "      <td>51.15</td>\n",
              "      <td>51.129</td>\n",
              "      <td>51.14</td>\n",
              "      <td>19622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 10:02:52</th>\n",
              "      <td>51.14</td>\n",
              "      <td>51.26</td>\n",
              "      <td>51.080</td>\n",
              "      <td>51.25</td>\n",
              "      <td>22319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 10:10:21</th>\n",
              "      <td>51.25</td>\n",
              "      <td>51.29</td>\n",
              "      <td>51.190</td>\n",
              "      <td>51.29</td>\n",
              "      <td>21519</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      open   high     low  close  volume\n",
              "date_time                                               \n",
              "2009-09-28 09:46:35  50.79  51.07  50.710  51.07   19844\n",
              "2009-09-28 09:53:49  51.07  51.15  51.050  51.14   20324\n",
              "2009-09-28 09:55:26  51.14  51.15  51.129  51.14   19622\n",
              "2009-09-28 10:02:52  51.14  51.26  51.080  51.25   22319\n",
              "2009-09-28 10:10:21  51.25  51.29  51.190  51.29   21519"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHmOHhq9VPOi",
        "colab_type": "code",
        "outputId": "6e0e9057-33f7-4c1f-fe02-eafc836b00e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(dollar_bars)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "59578"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AiBiXDS3Va6m",
        "colab_type": "code",
        "outputId": "21cbea85-8853-4205-fbcb-a3376f2f9136",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dollar_bars = dollar_bars.reset_index().drop_duplicates(subset='date_time', keep='last').set_index('date_time')\n",
        "dollar_bars.head()\n",
        "len(dollar_bars)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "58607"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "snODH4d2ZVLR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "close = dollar_bars.close"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zryt2n1FVsGR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDailyVol(close,span0=100):\n",
        "    # daily vol reindexed to close\n",
        "    df0=close.index.searchsorted(close.index-pd.Timedelta(days=1))\n",
        "    #bp()\n",
        "    df0=df0[df0>0]\n",
        "    #bp()\n",
        "    df0=(pd.Series(close.index[df0-1],\n",
        "                   index=close.index[close.shape[0]-df0.shape[0]:]))\n",
        "    #bp()\n",
        "    try:\n",
        "        df0=close.loc[df0.index]/close.loc[df0.values].values-1 # daily rets\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "        print('adjusting shape of close.loc[df0.index]')\n",
        "        cut = close.loc[df0.index].shape[0] - close.loc[df0.values].shape[0]\n",
        "        df0=close.loc[df0.index].iloc[:-cut]/close.loc[df0.values].values-1\n",
        "    df0=df0.ewm(span=span0).std().rename('dailyVol')\n",
        "    return df0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5y0YR3ddW9QS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DailyVol = getDailyVol(close=dollar_bars.close)\n",
        "DailyVol = DailyVol.dropna()\n",
        "h = DailyVol.mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nlboG7fY-IY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tEvents = filters.cusum_filter(close,h)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "moO-OAfIZcIr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "t1 = labeling.add_vertical_barrier(tEvents,close,num_days=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eimta3CwZmQv",
        "colab_type": "code",
        "outputId": "6f5d8b55-4677-4515-f72a-2ac345523803",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "t1"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2009-09-28 12:33:18   2009-09-29 12:34:54\n",
              "2009-09-29 09:27:39   2009-09-30 09:32:03\n",
              "2009-09-29 12:00:17   2009-09-30 12:48:14\n",
              "2009-09-30 09:44:47   2009-10-01 09:52:26\n",
              "2009-09-30 09:56:31   2009-10-01 10:00:15\n",
              "                              ...        \n",
              "2019-06-18 09:39:43   2019-06-19 09:57:29\n",
              "2019-06-20 09:30:00   2019-06-21 09:30:00\n",
              "2019-06-20 12:20:48   2019-06-21 12:28:50\n",
              "2019-06-20 15:50:44   2019-06-21 15:59:41\n",
              "2019-06-25 09:42:50   2019-06-26 09:54:29\n",
              "Name: date_time, Length: 3164, dtype: datetime64[ns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O6xbevewZtJA",
        "colab_type": "text"
      },
      "source": [
        "### (b) number of concurrent events"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNXS1XBtZpt-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from mlfinlab import sample_weights, sampling"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXfjGPPccZ0v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "molecule = np.array(close.index[0:1000]) #조금만 해보자"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_ucQvDQa0wH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "c = sampling.num_concurrent_events(close.index,t1,molecule)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1QWOxLDGc3gw",
        "colab_type": "code",
        "outputId": "92e65f52-8cd3-4898-e0b6-070d293b12bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "c"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "date_time\n",
              "2009-09-28 12:33:18    1\n",
              "2009-09-28 13:20:29    1\n",
              "2009-09-28 13:44:27    1\n",
              "2009-09-28 14:05:46    1\n",
              "2009-09-28 14:11:43    1\n",
              "                      ..\n",
              "2010-01-19 15:08:50    1\n",
              "2010-01-19 15:34:01    1\n",
              "2010-01-20 09:32:42    1\n",
              "2010-01-20 09:57:21    1\n",
              "2010-01-20 10:54:08    2\n",
              "Length: 991, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0g0_NMQlc8HG",
        "colab_type": "text"
      },
      "source": [
        "### (c) concurrent event 수와 수익률 표준편차와의 관계"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hIvordPCdELL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "coEvents_std = (pd.DataFrame().assign(numCoEvents = c, std = close.pct_change().dropna().ewm(50).std()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUkJviVmeAFq",
        "colab_type": "code",
        "outputId": "ca1fe66c-39aa-4fb1-e28c-e8a52f5d1ead",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        }
      },
      "source": [
        "coEvents_std"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>numCoEvents</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date_time</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-09-28 12:33:18</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 13:20:29</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000821</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 13:44:27</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 14:05:46</th>\n",
              "      <td>1</td>\n",
              "      <td>0.000975</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-09-28 14:11:43</th>\n",
              "      <td>1</td>\n",
              "      <td>0.001049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-19 15:08:50</th>\n",
              "      <td>1</td>\n",
              "      <td>0.002140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-19 15:34:01</th>\n",
              "      <td>1</td>\n",
              "      <td>0.002138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-20 09:32:42</th>\n",
              "      <td>1</td>\n",
              "      <td>0.002358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-20 09:57:21</th>\n",
              "      <td>1</td>\n",
              "      <td>0.002347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-20 10:54:08</th>\n",
              "      <td>2</td>\n",
              "      <td>0.002455</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>991 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     numCoEvents       std\n",
              "date_time                                 \n",
              "2009-09-28 12:33:18            1  0.000854\n",
              "2009-09-28 13:20:29            1  0.000821\n",
              "2009-09-28 13:44:27            1  0.000847\n",
              "2009-09-28 14:05:46            1  0.000975\n",
              "2009-09-28 14:11:43            1  0.001049\n",
              "...                          ...       ...\n",
              "2010-01-19 15:08:50            1  0.002140\n",
              "2010-01-19 15:34:01            1  0.002138\n",
              "2010-01-20 09:32:42            1  0.002358\n",
              "2010-01-20 09:57:21            1  0.002347\n",
              "2010-01-20 10:54:08            2  0.002455\n",
              "\n",
              "[991 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AM-6uO-veD9f",
        "colab_type": "code",
        "outputId": "c46c2f4a-78a1-4e5a-9608-c32d37de9776",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(9,6))\n",
        "\n",
        "coEvents_std.numCoEvents.plot(legend=True, ax=ax)\n",
        "coEvents_std['std'].plot(secondary_y=True, legend=True, ax=ax)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd51f5b3400>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w8dp3D6Qkyg7XuxDAotlHY7Op/Oud/Ryud8XtX1UXijeqrHVKUZYk5uEDmPWV\n1oN2hBMInwd8nrTWvYyF4rB2bAuPs00BeNLkoYwvGNvmmG6PwUubD1CT4HMlkUh6N71ClG3dupW9\ne/cyZ84cACorK/nRj37EPffcw+zZs4P9LrvsMs4///yIc+12e9LznHPyUcELH4Bi+ECzMW1SEc0f\nQX2DE7fHwGHX2hxLCBMEnbrtXhKb6eMLOXHSEEYW5vDrf+xo3UGEis6nDf+Yc08sJivDxrp3DyTc\nVNfiltne24N397ug6qiDj0J4U0/OGqp72bmiDL+lLJm0GDPGFWAff1TcdiFM8LpprG9g83uNcrem\nRNKH6RWibNmyZSxbtiz4+Mwzz+Thhx9mwoQJEf1yc3PJzU3RpZHg+qaYXhS7LVhsWMPHgcoGJo7K\nb3PYlld+jVH6GQOWrQ4eM5uPIJqPoA1J3gIjSZ5kb1WBfuHCKP0JPJWEqTjSKgj7CcI08O3dgn7U\nNFBUzJqS1Mfwl1jqfPdlwFLWegemMM2Ix74vN2KUfQYeF8LbYp3jdSE8LQivC7yWZUwBbhsIe4/o\nQGy3uEQi6d30ClHWbZg+0DJR/KLMphjsKatvU5SZjdUYpZ+1Ot78zHLwuSOEmiR9CL8FIZ7AUqIa\nnDGsVaLDVojk1F2suSWJMUp3Iloa0MfPxCjZYQmWFAkE2HdJoD8Ed2D6Dm4HWwb68MmIqCTURs1B\nlIZDVm4zewZKRg7KgEIUWwbYM1FsGSj2DFyGgrL179g8ofN95V8g3E3Yjj6xU5+PRCLpGnqlKHvj\njTc6bezwe7Jiei1BpluiLD9LZW9Z7Kz+Zl0F3v3b8O3/EPPwgdiDy1p4XUsb+iid1qpoKafEOhhG\nhJUuSSHX3/HueRcc2egjj8Oo+KrNMkaxCLovOzumzC/K8DjxfPE27nf+gjZ8siXKnPURfQdc9oek\nxnQ1uWDr39HM0C5wz4f/QrQ0SFEmkfQReqUo60zC76OKacWUoWigKBTm6Gwoqw9aU8yaEnwHPsS3\nfxvmkXIA1CFjsJ90IWb1Pnz7tyEMH4qmt3JZSDqTxCIn8B53hrUqYIxTlMTu1AhBKDVZmwhPC779\nH2GbMAtFs0WknFDUtmM8g+N0kfsSmwMA71ebMCqsRNcBy15AlNVkjWZ7TQ7nxx6hNaqKR2ioflEm\nhMCoKQkJQIlE0uvp96Is0Y0zIMoURQHNxuBslcHecmrffgpHxQ5EYzUoCtqwiThmnYE++gTUnAIA\nPJ9uwLd/mxUPouVglH4SmtP0oaj9/qVPO9EFnuNaoPz9YlnK2uu8jOn1TDCYdF+mhu/AR2B40P3J\nYhV7htXgdYEj+Z2rwh0I9KAnWAYAACAASURBVO/c3a6KZn2/jYov0YqPBUVFNB221uCsA2DXsAW8\nXNaQvCgDPEJH9e86FU2HrZQbSvKiVCKR9GykMmhF6E6qGN5gPBmajcLKd/lZrsDcraEWH4M+fT76\nUdNRY+RLUmzWTUN4W1AychCuUGoGfB6wy5e+s4hneIqONYsM9O9ac5XcfZka3j3voQwYjDZ0HOCv\nLYllQVNSEWWuJrBldv6PIt0Omh1t2Hgyz7oe18bVmEeslCymX5S5tRwUJXY4RDzcQg+6L42arwEQ\nMixCIukzSGUQRWRMmS8YT2Yb+01M5xH+9kUOmWNnsPScNpLI2gOizB+MLELuS+HzSJdDJxDc7diG\nvgr0i2mtSlO2AUWRuy/ThemswyjbiX3afBTFn2ImkHLCm1pcmVX3spNdl4Ci6mRfdA9K1kAUVUPR\nHdaPMUC01IM9C0NJ7fKrYFnKHH5RZvpFGYYXIczQayORSHotUpQlQDF9QUtZxuxLAGio+5iDlW3/\nMg1YyvD440h8YSWafLKGZmfQyoWoJHzY6cIoYUxZmixlQggQZkpxVb0N354tIAT6uJnBY4GM+Ynq\nS8bCKrHU+aIMQM0ZFHpgcwTzqglnPWrWQAQi5VQsHnQyTev6EZESxOeBwDVHIpH0WuRPq7gpEASK\n6Q3mKAswbkQe5dXNOF1tCKuAeyUQ3Bu2fT9CoEnSTrK7GWOmxGinqSxwXrI32XS5L72fvELz326y\nEoz2Ubx73kUdPBotf3jwWPjuxlQQ7uZOT4cRC0V3gOFBCBPTWYeSledX7ampMk+E+zIkyuQ1RSLp\nG/R7URZ9Cw5oNA3/TU6LNCaOG5GHAPaVJ44FCcWUWWIsEE8CyMLmnY0S8V9r/O9xS5iwTndEmaKQ\n0FQWbqVr79zC9OH5dIMVON6O9BAdRQgz5bxuwuPEV/550v2NI2WYhw9iGz8z4nggjix1S1lTSjFo\naUO3dmPi8yKc9ShZ1nVETdVSJnRUw2Mllm2sRskdajW0o7qBRNJf2L9/PxdddBHz5s3joosu4sCB\nA636GIbBHXfcwdy5c/n2t7/Ns88+m1Tbc889x4IFC1i4cCELFixgzZo1wbbf//73zJw5k4ULF7Jw\n4ULuuOOONtcq3ZdxsClWXUQlylJ2dFEuigJ7yuo5dsygWKda59nCdofhdzXYMsDrwvX249gmn4Ft\n/KxQ4WJJhxFthZT5zVihlBhGqy4dzR0bbqXrbPel7+DHwZ183SE23O/+DbO2lKwFy5M+x/Xmo/gO\nbif7kt/F3CATjW/3e6Ao6GMjC9MHRVmqljJXY/dYymxWuTfhc/tF2UCEU6SsyN1CRzWbMWpLAdCG\njcPXcCjl10Ei6U+sWLGCJUuWsHDhQl588UVuu+22CPEEsHbtWkpKStiwYQN1dXUsWrSImTNnUlxc\nnLBt3rx5fPe730VRFJqamliwYAEnnXQSkyZNAmDRokXccsstSa+131vKogm4oWz4b9hRoizToTOy\nMIc9ZfXRp0YSFugvDC/mkQrsx5yJ49TLQdFwb/4rTU/egC9G5n9J+2jLhdgqpsydxlxhUQos0XCG\naeL2tBaEqeLd9WZoen/+ra5CGF68uzdjNqRWON6os/L5icbDbc8hTLx73kMbMQU1a2BkYzCmrDnG\nmXHGM3zgdXVZTFk4it9SJpx1YHhQM/MQIvXEwR4s92Ugnsw25hsAKb8PEkl/oaamhl27djF//nwA\n5s+fz65du6itrY3ot379ehYvXoyqqhQUFDB37lxeffXVNttycnKCu/ddLhder7dDu/mlKIvGf3PV\n41jKAMaOyGNveQOmGd8WErCUeb/caCWWFQbq4NHYJ59O1ndvJ2vhL8Dnxqjen/7n0F9JQRhBpPsy\nbQQmVZS4ZreWGBa6VDHrKzHKdqKNsnYBB/JvdRVG2S7LZWq2z+IXyKyfcI7K3YimGmz+3GThKKoG\ntoyU3JehHGVdL8oC7kuz/pC1hqw863h73JcBUebIRi0YBaQmTiWSvkJFRQWlpaUR/xoaGlr1GTp0\nKJpmbYbSNI0hQ4ZQUVHRqt/w4aG41aKiIiorK9tsA3j99dc577zzOOOMM7jyyiuZOHFisO3ll19m\nwYIFXHHFFWzfvr3N59Rv3Jf5+bFdOzdf8g1uviT0+OoLp3P1hdPx1JRT+vDz5ObnklMY6e648eIT\nuTGJOZuw3JbZniqcwODxk7APssYShdPY/yJkZWgUFHa9O6Wns/aBhSmfU1g4gLXTiiMen/aNo4KP\n77n21Ij+T911btrmvnTBsVy64NjQ2HeeE3+d7ZwjnJodz4GqMfT0xZSv+Zgcm48BXfg5qnp/BwCK\nMChMYd4WBXzAAIfZ6nsVTfXWbSg2B8NOPA01hpu/JTMHh+pJen5P1RGagbwhhW3OnW6cdQOpBLLM\nBlxA/vDhXDdrKtd9f0bSYxQCeWdMpn7LXtSGcjKGHU1h8TCagWzdx0B5HZH0M5YuXUpZWVnEsWuv\nvZbrrruuS9cxZ84c5syZQ3l5Oddccw2nnXYaY8aM4fvf/z4//vGPsdlsbN68mauvvpr169eTnx+/\nfna/EWVHjjTj87Xeobb6lS94Z4flUvnz8jP5x5t7eHVLCcO1I9ySBw1OHy3Vka6hqroWlj/8HpfM\nm8gZ00cknFcrmkjdga9At1Pny0EJH0vVcDY6Maq71vXUG7jiXqu+6Z+Xn5n0OXvK6vnfv37Izy48\nnqljBvH5wSP86untwXF+/Y+P+WxfLTcsPp5Jowby4wfeDp57549OorgwB6/P5L/ufyvlude/f5B/\nvrWXh2/6Fnabxs9+v4lp4wdz2dmTWvU9WNnIHau3Bh/fsPg4jhs7OOm5hM9D08dvoI+eQYNqfbkb\nqqtxddHnSJg+mr7cAoDp9VKdwryGz7Ks1R8+3Op7FTGH4aVp52b0o2ZQU+8DWvc19Uxc9fVJz++r\nsH7ZNrq1hHN3Bj6nZR1tLLdyi9V7bDzytw/Z/Fklf/jZaUmN0dTiZd3rBzgvy4e7fDe2Y8/i8BE3\naDpNtbV45XVE0k/QdZX8/GyeeuopDCPS85CbGxmrWlRUxKFDhzAMA03TMAyDqqoqioqKWvUrLy/n\nuOOOAyKtY4nawhk+fDhTp07lrbfeYsyYMRQWFgbbTjnlFIqKiti9ezcnnXRS3Ocm3Zdx0InvvizM\nyyA3286e0sRxZUruUJTsfMyar1ELilHUqJdb1RHtdP9IYpBc7lhAtAq073BIWbSrMr73ssNB/r59\nH4C7GdsxZ1qpVxQtsmJEJ2OUfwHuZtSCYjBTdAH7X5S23I6+kk/A42y16zIcxZEVEeBuVO9PuBs0\nWIy8u1JiAGaD5b608pSl/rkbbwu5TLTBo1AUBcWeLd2Xkn5JUVERxcXFEf+iRdmgQYOYPHky69at\nA2DdunVMnjyZgoKCiH5nn302zz77LKZpUltby2uvvca8efPabNu7d29wjNraWrZs2cKECRMAOHTo\nULDt888/p6ysjKOPPjrhc+o3lrJUCey+jA70Bytb+7gReextI9hfUVUwDYyaEmxjYyhjTQdDirJ0\n0VaOsUBQtRAx8oQFAzPTlNI/wVjRc6e649Oz603UgUVoRZOsm3JGdpcG+vv2fQC2DPRR0/DUlqZU\nFDyQgb8tEeHb/S5KZi7aiClx+yg5g/Dt24r3q81gc+D6z4NknH4VtgmnxJ7b1Y0xZbaAKKuyvvf2\nLBDJ57ULcMjIY4JfmKkFIwErka7ohpQoEklv4fbbb2f58uU89NBD5ObmsmrVKgCuuuoqrr/+eqZO\nncrChQvZsWMHZ511FgDXXHMNI0da37FEbc888wybN29G13WEEFx88cXMnj0bgF//+tfs3LkTVVWx\n2Wzcd999EdazWEhRFn3jDAT6x9l9GWDsiFw++qqahmYPudn22EMrGmbjYfA4UQeNat2sau0OlJYk\noK08ZXReNn8lbO64lrKouVPRZMbhg5hVe3HMXBLc4aNkDEgqcD4dCNPAt/8j9FHTLGEB1g+LJESZ\nMH3BfGqJLGXC3YyvZAe2Y85IKPYcJ1+IaKrB9dajwWNGbSmxv7Fhgf7dkKdM0f0pMZpqUHIGBctw\npbpLa6NrIqdmfAmAGkima3PI+pcSSQLGjh0bkVsswKOPhq4dmqbFzSOWqO3WW2+NO29A/KWCdF/G\nIV6esgDjRli7pxJay1Q1WJ9OiyHKUHWE0fGdeBKLUJ6y2De68PtfusocxSPRzbbV3CmoMu+uN0Gz\nR1iDlIycLtt9aVR8iXA3oY85EcW/mynZHxYRwtET31Lm3bcVTF/MXZfhqFkDyTzv/2GfviB0MMFa\nhKsJdEdQIHUpgeSxgOJP79GenHguEVp74Nqk6A6ZPFYi6SNIURZFwAWmJ3BfAoweNgBNVRLnKwta\nwhQr/iYaTY+4iQhXk2VZk3SMgLUqji4SxLCUBWKd2um9DJ3XdvLYVu7LJFWZ8LTg3fMe+tiTI6w9\niiOny9yXvn1bQbejj5watI4JM7kfFuGiLJH70rfnPdS8YaiDR7c5pqJqOL5xAeqQMdaBsHJmsebv\nFtcloNhCoiyQc02QuvvSKWIISmkpk0j6DFKURSGi3JeKHluU2XSN0cMGJBZlivXyKnlDQhn+I5p1\nCLuhNT97K81P39zOlSeP2dJA01M3Yhw+2OlzdSWBIO8273OitbUqnZFkbQ3aWhAmN5x392bwubEf\nc0bEcSUjp0vcl8I08R34EH3U8ZZ1RglYypIVZX7hmCC/mNl4GKPiS/Txs1Jy7WWe9VMgFGcVc353\n94kytDALVyBHmRApB/p70TiSO4GMuVeHxtMdIEWZRNIn6PeiLJ5lJFGgf4CxI/LYX9GIz4hdDDoQ\nD6PFu1FokTFloiVxPc10YdYfQjTXRtbj7GfEKwjeUXGmtGGlA3C6I3csJjOnEALvrjetwtwBq1AA\nW0ZEwfvOwqj8CtHSgO7PIh+M90rRUqbmDQ1ayoTXhefTf2P6BZt3z/sA2MZ9M6W1BcqVJSrMLVyN\nKI5uspSFfSACokyQ2M0dZyS+HHcJtjFhG4d0ByKB+9K794M+9wNMIumr9HtRFo9EKTECjBuRh88w\nOXgojuvIbymLFeRvNehW6RfAbKoJHhZGJ2SaD8cff5ToBtabSeY253T50MKrQXdQjUWfbtUjj5/R\nP9MRCmBPxmVqHNqNeaQMW5SVDPyuMZ8n5eLgqeLbvxU0O/pIK1cPmn+fUNIxZdb3RM0divA4MWrL\naH5+Be73nsa78w2EEPh2v4s2dDxq7pDUFqfZLCWcwGIkXM3dkg4jmlBMWeq1L2OOZ3Mg3M2YTbWt\n2oTPg+v1h3D+q+1CyBKJpPuRoiyKwH0tWUsZwN6yOBaugKUsjigzq/djfP0JAMahUK6Tzt7eHozn\n6QWiLBWhEewax/oQSnph5SnLyghtPhat/kgDcd2XXrIcqW189u56A+yZ2MbGsCAFAtcN6/10vvIA\n7g9a7zTqCEKY+PZ/iD5yasgVH6jnmGSQebilDK8L53O3BXdjGmU7MWtKMOvK0RPkJouHoiiWxagt\nS1lG1++8jEbN9FvK0qPJrGLtQtD83C/xHvgoos2o2mf9IWJb8yUSSc9CirI46PgvYlr8m2f+AAeD\ncjPix5WpbVjK/MHawufBOLQ7dNyTfD2/9hC4OfYGS1kqGimoyQL/R4mz8MfRwqjDVqZW5yfefZnp\nCBf7iec2Wxrw7duGbfwpEQHjwZkC4iggcL7+FM/HLye17GQxDu1BOOuCrksIcxkm6ToVriawZaDm\nDQNAHVRM1gV3ok84BbOxGu/ud0HVIl1zKaDo9riWMmEa4HH2LEsZ7XFftkYvmkj2BXegDhiMa8Pv\ncG3+K8K0rl9GxRfWnDmDOjyPRCLpfPq9KIu+HQZcTjbFQChqm0kxxxXnsae0LvZNXVHBkY2SHbvO\nlePkC605XY3dZCnrBcHBKakyf6B/W/c5f6B/uKUs1NQxcRYeU5Zo92WEla6NKb1fbrRSRMRwXQLB\nnb0tL9+PUVvaqt1sqKLxLz8OWU2w8p2l8jnz7dsKmo4+6vjgsaDFLMlxLEvVAPRxJ5N59o1kLboN\nNWsgas5gRPMRvF++gz7yuPYH4yeIrQp85rsrpiycUKB/+sZU84aRtfAX2I6Zg3fn6xilnwH+6gsg\nk1RLJL2Efi/KAgTv4wH3JQZCbdvFNG5EHnVNHmobWt8MbGNPxjHtvLi/hgO/2s2mWszDB1GHjrOW\n0EYJmo4SDLLuFZay5O9c9to9FGlHkurb4val7EJMRKxVJkoemx1DEMYcV5h4P38LrWgiWn7sOqv6\n8MlknnMTwtWA81+3t2pv+fdvLXfhC3cGx3Q+v4KWV/8v6TX49n+IXjw1aB0DwC/KkraU+Xc/KqqO\nPuq40EaYYeOtDp4W9DZykyXC2oUY+zPdrdn8o1AyrTIwAoGaDv9lYFzNhn2GlbPNbKyyLPBVe6y5\nuii5sEQi6RhSlMVBUwxQ48eTBQgkkY3lwrSNn4X9+HPjnqv6RZnx9ScgjGAAtegi92XviClLvu+Q\nbQ+xPG9tUn2dLh+ZjtbWKiFgbsannJe5Pfk1mgZD6j4FRNzEtaF5BC1uI9J16v/fqPmaxkcux6gM\nubKNrz9DNFZbdS4ToI+cStb3VqINP6b1nNGWLL/VxKj8KuGYAcyqfYjmWvSjT4w4HnRfJvl5tfKE\ntXYfqgNDhYHDLXEpo9vj5usKZvPvCaIskN9NQLuiyhJ8J5TMXFA1RPMRjKq9YPhQh4wFYQQ3FUkk\nkp6LFGUB/NfGwPXOhoFIEE8WoHhINnabmjhfWTz8Qce+EivYXy8+1jreRe7L3mApaw9txekI4rsv\nARZkbeeszE+Tns+zYz1Typ5nhv1A2BoCM0Xi9hqYQpCZ0Tqezb31nwB4928Ltnk/fxMlMxd99Alt\nrkPNzCUzLH9VqCHkgvfu25qyGPfu3waqhj56esRxxV9myb1pDZ4v3m4zLs9yX7YWRQHLkZIzqEPZ\n9gO7UOPNDd0ryrIuuIvMeT8NPhYp1r5Mpq+iqCj2LIS72XJdKoqV6Bd6R7iCRNLPkbUvWweVAf6Y\nsiQsZZqqMqYot83i5LFQdMv9Y9YcRMkdau1KowssZb0opqwj8ffRN7HAYyGgxeUjKyzYviOxZKLZ\ncplmKZGvZ6wRA4ljB+g+lmRv5iXnjGCbUbLD+sOf98tsqsFX8jH2489DSeIHAhAzSbE6sAijsRoA\n12t/gCTc8sHnIAS+fVvRio8NirAgYQLK/c5f8O15n4xTLw9+jluNFcdSpmg2Ms/9b7TBRyW9rpjo\ndoQz9vcw5L7svkB/bdBIGBSes7CTUpjYMxFeF2ZdBeqgo0IbC7zubqn7KZFIkkdayvxEu510xUj6\n5jV2RB4lh5pwe1KrYxm+k04bOhbsmYCSsARNOuj7lrLE7T7DxOMzo6xVgf/bvlEKTwvGoT14Pn8L\n16a/WukqAIVQjoPRlJPpa52/LpC0dnz9e5zs2Mssx+6Q8AyIHH8aFu/nb4EA2+RvtbmmcOwzFvqf\ni7UDL5BsNufKx8icd0PCHcXRmIcPIJpqsEW5LiHSIuk49XKMwwdofu42zBhJkIXhBa8rrqVKL57S\nYStWosz2QVHWAwL9AwhATcPuy2gUWyaipQHj0F604ZNC15le8CNMIunvSEtZFMHdlxiIBDnKwhk3\nIg9TCA5UNjBxVOydljEJs2poQ8ehKKr1K7fTA/17T0xZu6wJbeRkCgijLIfOULWORhF6H4Qpwv42\nMOsPYdaWYtZ+jVlbilFbivBbnYCI9zBLCb2eF6vr8Bx2AJF5xQLlnXJbrGoK52V9zG7nHGAo+phv\n4PtqMxgehOnD+8U7aKOOQx1QmNLTD1rLvG6wZ2JU77eOqzr6UdPI/Pa1tKy/33qOPk9Cl6Fv31ZQ\nNPSjpsftA2CffDpqVh4t//4tov4Q+F2SAbrEUqXb4v7QEK4mS+x2RzHyOAhBehKVRaHYMzDKdgGg\nD58UTI+RbD45iUTSfUhR5keJiinTFQOhJncBHxsW7J+KKFNUzbpRGF40/85LJTMX0dKO+LQksfI1\nWTFrvcFS1h73pep/XvGC7p1houzWgS/RaGbgZrZ/wpC1s+kv/xVKJaCoqHnD0AqPRp14KlrBSNSC\nYpQBg3D+6w7MwwdxY4uY0S5a3wQD7sus5lCJK4ezyvojePP04DvwEaKlvlWdy6QI3xWp20NuUT9q\n2C5O4axDiZM9XwiBd99WtBGTk7JiBQRXrN2YIUtV57nPFP93KRaBYuTpyAuWLkQ7al8mRUCUKwra\nsAkYVZYol0XLJZKeT78XZfFiiWwk777MybRRNCiLPaXtiCuzZSBUDTXfyjWl5hRgOutSHidZIuLV\neoMoa8c5ihn7xhy4ITe7rPaA+3KA6sIVmCjMymabMjcovtSBRXEtSo5ZS2l56X+pMnJjtofT4vaR\nqbjRjLAbZEAIBub2ufHuehMlZxBa8XFtjhmNYg9YylwY1QdatavZ+WTMvRrXaw8lFOZmTQmisRp9\n+vzkJrYFksm23qgSCrTvREuZZou/w7A7i5EnolPcl9b7rw4ahWLPCrkvpaVMIunx9HtRFg9dMZMK\n9A8wdkQeH+8+bP36TeVCa8tAKyhG8Wf/V2wZiJY4tTTTgcsfr6aoveOXcztUWVCURQf6+/9vcYUs\nZcFpAoIorLh2xje/n9x8/oz6Gmab773T7WOMXh1xTPP5LUv+NRg1JYj6Suzf+F7wc5EKSpilzPP5\nm7H7aH6BmUCUWa5LFX30jLh9tFHTghUoAmIwVlLarnBftmkp60HxZGBZgduTp6zNr0TgsxwIv/CL\nsl7xfZdI+jky0D8a/xVPTzIlRoBxI/JoavFy6Ehq6SwyZi3FcfJFoQO6vVPdisF8TVkDe4mlLHVV\npojE+ZiC7suwQH+93p8J339D+3fL1OQn9AunQs0KcBcJYtqcLh9j9EOIsDQVasBqFnBf1lda5YYm\nnpr8GsLxizKzthTj6zipPfxWPxFPxAiBd/9WtOGTgvn0YpF19g1kfedWIDzDfyz3ZRekpNB0MHwx\nN2vES8fRnXRWAXnH7EtRsgYGryuBHw2BQH/3Ry/R/NxtwVgziUTSc5CWMj/RBg5bksljAwTjykrr\nGVaQ1UbvEPpR0yLXkaB+XzoIlpvJKcCsLWujd/fTnvuWEkdoBExlwbgue0gYKW5LNIj9HwDwTcee\n5NfYcBiAhVn+YtAJbnYtbh+TbFVQcBSr9k7hlry1FJa+hfPVMswjofdDH30CaqAcT4oofjei55N/\ngwIZZ/4Es64ysk/AihJHmJtHShH1h9Cnnp38xAndlwFLWSemZFA1QFjCWoksjybczT2i7mU4qda+\nTLanmjGAnIt/EzrgF2Xez9/GbKrBs+1f1vzupmCOOIlE0jOQljIR+2GqlrKiQVlkOfT2JZENR3d0\nqpshIMrU7PxeYSlrD4ppia6Miu2c6viiVXvAUpbhCrkRA0JOuCxrV56avMVTGzE54rF39+a4fV0t\nLRRrNTBkPOVGPmuaZtM4cDyioQrRdDjYr60M/okIxBCZR0rRR02zyn2dsDCyT9ZAQMG99Z8YR8rw\n7v/QKgjux7dvm5V4NIHrstW8fouhWVPSqk24GsGehZJCjrSUCVgfzcjUNEKYlgDpaTm6RPJCqyME\nPg/God14tv0LJdefD7EzwyQkEkm7kJayIGGZRUk+eWwAVVEYOyKvXUlkI1aRoH5fOghayrILrNIr\npq9zb5QdpF27L/0xZXkfreZ72bDRPSmivdnlQ1HA5q4jcPtWAkI430ruudY5nSVJzheesFW4mvBs\neRaAr+1jiS56lNVUiq6YmIXjgEY+9Ixh2vjJjJpahPC68Wxfi6/iC7SiiSk84yjC1mObHKeIeW4h\nGd++BvfGJ3A+vyK4y9Tmrz3p278VbdjEdlnrfAc+anUsXuLYdKLEEWW4nSBED7WUdcFEYRtU9PGz\nsE89C+fzt2PWlqAVxK6nKpFIuoeeezfuZnTFwEzBUgYwbkQuL+yrwenykpWRvKCLnNgOpoFRtRdt\nyNj2jZGAgBtJ9Wf5xucBe0/+GKSuyrTGSrx73mt1PHD/c7q8ZDl0RENVqDEQ1+UPgN/jG5byvADu\nbc8jPE58aKi0dmMWtFhWJKVwHGDV1wwIT8XmwHHS97CnulkkinCRqAVKd8XAdvSJaEPH4964Gt/B\nUK1P40gZ5pFyHKekbq1T8obFDLYXXbH70f/jQpi+CAtUTypGHo7orERlUSiKiu24c8DrwnHKJcHw\nCLPpSKfPLZFIUkO6L/1E5ymzUmKkJqzGjshDAPvKW2c0Txb96BNRsvNxvrAS16Y1aS+5JNzNYM8K\n5bLq4S7M9oRC53z2T1xv/Cn4eJR2OKLd6bLqXpr1lZjCeuOVQG6tL63s/IZo383Su+tNbFPmcIjB\nKDEC/gu9pdSqgyDMlRa9maHDubRsDlA17NPOa3P3ppqVR8ZZ10cc8+3bBihJ1duMRhsa+UPCs/N1\nPJ+80jWB9vHclz2oGHk4qda+7AgZ37yIjFMvQ1FVq5C8LQPhlKJMIulp9HtR1qr0pQAFE00RiBTd\nekcX5aIodCiuTMsfTvbi/8V27Fy8n79J8z9uxWyqbfd40Qh3M4ojO5Rzq6eLshRUmaFbgeZN035A\n1gV3BY+PtR0CQBEGF2RtIUs4yXLYMOsrKTPyMQUohv91KLN2K7b3XqlkDsBxwiJMVJQoS5kwDUaY\n5VQ7iiMnSPMmPEVRyfnRYzhOWpxk/9BihGn6XZfjrbjDVOd25ESUCXNv/ivu958JJm/tVBT/5Szq\nQxPc+dnDUmJAOz9nadi1qWYNDNZslUgkPYd+L8pioftvpqnElAFkOnRGFuZ0ONhfsWeSMWspmWf/\nDOGsw6j8qkPjhRN0IwVSIvSx3EXvuCbiGTXLKv7sp8awYomGew9yWsaXXJj9PpkODbP+EFVGLh5s\naO6GCPFrUxKn1YiH5TuNygAAIABJREFUbeo8FEc2JipqlKWs5dX/Q8egIbM44nhnJEZI1dqmjzkJ\nAKN8F2ZtKXqMWpdJzevIBq+rVRJXy1LWyTFlAVEWbSnrye7LbiowoGTnd2qSaolE0j6kKPMTZiuw\n0mFAKPliCowtzmNveQOmmYZfs4NGWStKowtTuAKWskDuoviWsp7g2kwll5NiGhgilAqhccr3gJDA\nEv6Pu13xkeNQEE2HqTZzaTbtZJW+T/Pfbgyda2a2a71qnhWLFstSZpR+BkDLgJHddS+Oi23y6QB4\ndrwC0DFRBhHWMgB8ni5wXwYsZVEWSncX1N1sB6mmxEinglOyBiJSEGXC48RsqOq03GoSicSiJ0d4\ndy1h1zvdvycvlZQYAcaNyOPNj8oorW5i1NCO3QQUu5XvLJ0FyoW7GTW3MMxSFlt4eb/ciOvtx8n+\nwa9SLoidTlK6BQgDAyUYp+Mddizs/KcVHwiY/pgjHZNCrQmEoMrIZXXTt/jRKQMYWpCF+52/AFBl\nti9HmJpnpRswUVoF+mvHz8fYsQ6RE/l69oQbnZpdAIBRthN16DjUnIJ2jRMSZU0QtXOz00VR0FIW\nJcpcTVbesrANED2BbjSUoWbn42uui6hAIlxNmA1VmA2HMOurrJjLhiorXYvfBZw5/xb04ZMTDS2R\nSDpAvxdl0fdDIQhZylJ0X4IlygD2ljd0XJTpditLeTotZf58TaGYstjuS8/O163+zXXQjaIsFRRh\nYoQZf4Xf0hl4P/N9VsC/rhgMVi0Xc7WRS4kxmOYR07EflY/L5eGDje+3ew1ageWajOW+9AkNBcvN\nHRHH1e7Z0khY2gRbO61kAIrDnzg5Rqmlroopi66o0BOLkYO1waO7lqRk54Ppw/XaHzCbajEbDkGE\ndVNBySlAzR2CNvoElIxsPB+/jGg8HHdMiUTScfq9KAug+H+zCjpmKRucl0Futp09pfWcMb3jOYAU\ne1ZKljJhePF+uQm9eApq7pDINmGC22lZM/RAPbzYljLR7I+v6u4bWZKKRZgmCgJDhIsyS2icm7kd\n5/r7ObHRch9mK258puW6qTYHRE4z/lv87dXIbPDtW7bSavFen4GdyPJOPYVgEXPa77q0xrFEmfuj\nl1AyI3+UdLqlLLD7UrSOKetp8WSA/+PRPd8vdfBo0OwYhw+g5g7FNvZk1NwhqLlDUfKGoA4oDP1w\nw19H9eOXgxYziaQ3sX//fpYvX05dXR0DBw5k1apVjB49OqKPYRisXLmSjRs3oigKy5YtY/HixW22\nPffcc6xevRpVVTFNk8WLF3PppZe2eV48et7dIQ5XX301paWlqKpKVlYWv/zlL5k8uXPM6B2xlCmK\nwrg0JJENjmfPSjqmzKgpwfXmo5i1X2NOnUfGzB9EdvC0AALFkdPm7kvR4q/j2M1xZUlbkfxZ/A3U\nkEXEbynLUr2YNSUEskIVao2YxhGUjAG0CH9sXbpdiIqCErZ698frsO9aB0CmI+pz1QNMZQExpWQM\nQB0wuP3j5A1FyR2CefigZeXV7cHPWJftvox2X3ZFjrR2IGhfQfJ0oA8bT84Vf0reeqg7QNODmyYk\nkt7EihUrWLJkCQsXLuTFF1/ktttuY82aNRF91q5dS0lJCRs2bKCuro5FixYxc+ZMiouLE7bNmzeP\n7373uyiKQlNTEwsWLOCkk05i0qRJCc+LR68J9F+1ahUvvfQSL7zwAldccQW33nprmkYOZO4MPdT9\noizVlBgBxo3Io6quhfrmNAgaR1broOkohGng/uglnP+6wxJTtoyYLs9gNv+MHNAtYdCm6OruYP9k\nxZJ/x52BGrI9KKGPd/YPfsVBRyhLfra3NhiUHzFdO5dZkzk6ahwFJWztng/+Gfw7y6FH9e0ZZP/g\nV2R97662OyZAzRhAzvfvI+eS35Kz5AFyLv9jsK3zM/rHCfR3NfbIdBjdufsSUqy7qSgoGQMwW9qf\ng1Ei6Q5qamrYtWsX8+fPB2D+/Pns2rWL2trIVFPr169n8eLFqKpKQUEBc+fO5dVXX22zLScnFBrh\ncrnwer3Bx4nOi0evEWUDBoQu6E1NTWmPDwlpMoEWCNBW2+fGCsaVpcFapuWPwKjai4guHePHqCvH\n+eJKPNueRz/6RLIX342aU4CIEdMTTA0QsfuydUxZ+LnpTl6bKslbyvzB/EINvpnhHxFFd1BrLwo+\nznDVWNnnU50nDh8WL+F/6kOWSUGkpSycVtUeekCgP4A6oDBU6SFNKGHfIcXeybUnlXiirIdaykQo\nbCKl8zphLcmgZA1EtKTHAyCRpIOKigpKS0sj/jU0NLTqM3ToUDTNuhZpmsaQIUOoqKho1W/48OHB\nx0VFRVRWVrbZBvD6669z3nnnccYZZ3DllVcyceLEpM6LRa9xXwL8z//8D5s3b0YIwWOPPdaqvaGh\nodUbYrfbGTJkCPn5sW8It17xzYjHyy8/mZaDOVQ8uYFJYwaTWZj6r/vCwgGsnR7fPJkKDUdP5vCX\nGynIEuhhwlQIk4at66l98ykUm4Mh599IzjGnAFCWlYOqeCmMWruzwcQJ5A8rxDGsgCYgy6GSH9XP\nXVFFwEmRbfOR147XoKOsfWBh253C8DUZNAFXfXcaecdY4quwcABlW8eRMfpYBhUO4ILrrqX04S8x\nnI3orgZyR4xqNU9hYepzA/z4whP48YWhDPgnHlOEq7yJwsIBmK5mwiNxpk8Z3q7n2FsJPPchQ9u3\nozVZWpwDaQHU3W+RP+oo9AH5CCFodDeTXTCIgm74HCfivutPS/mc7vzMGAMH46uvanVdkUi6i6VL\nl1JWVhZx7Nprr+W6667r0nXMmTOHOXPmUF5ezjXXXMNpp53GmDFj2jVWrxJld999NwAvvPAC9913\nH48++mhE+xNPPMGDDz4YcWzGjBk8/fTTHDnSjM9ncsW9VhmdPy+36vot/9N7VB1p4cr5k5l1bBH7\nyhv4+982cV0ufLSrkgnZo9u11p8/8j6jhuTwk0VW7cGX3zvAc2/vA6zNAACH610Ra4mF12dZVQ6X\nlaMNst4us6Ea19uPYVR8iTbqeDJO+yEtWQNpqbZufz7Fjmhqoro6MijXW2XtnKprUVBrLFdmU20t\n3rIqsGUErY/eg/uD5zQersFTnd7g3kNHnPz8T++Tk2kjw65R1+TBZ1jWjT8vPxOny8e1v3kHCL1W\nbZHpa+RmB/zh+U+5eNjJZDp0mlq8XL9zFuyEwZv+TYPTw3dsuZyaYf1CcmoDefyvW3nr4/JW70kq\nc7e4fTS7fMH1A2zZdYihvibMr2vJqD8Y0X9/SS2ZDo3/z96Xh8lRleu/p6r3melZM8lkI8kw2UhC\nAsgOQoCAGG4QQRRxA0G9IMq9V4nXnwiCC+gVReVyARHRyMVcBSQiq7LLHrJCMjNJSDKZzD7dM9Nr\nVZ3fH9VVXVVdXV3dXVXT093v88wz1VWnzjlVdeqcr77l/X604R1094Qz2m6fGcS3P2vO2f7+J97D\ny1t7VWNIO8ZLBdrxaDWofw7cy8/F+I5nMb7rDXhWnCfyrwk8ooLbtvbvfXwn/rnjMC47uwNnHzcn\n9wmA+MyeeA8ulsE93zjD1DnROIdr7ngRl64+EuceP7eIHheGpKsGXHjY9ueoxR//0YUnX9+PX3z9\nNNQUmlO4irKCy8WgsbEGGzZsAM+rrUjBYFD1u62tDX19feB5HizLgud59Pf3o62tLaPcoUOHsGLF\nCgBqLZfRMSVmzpyJ5cuX4/nnn8eCBQtMn6e6tjzuQ8ngwgsvxI033oiRkRE0NqZTwXzuc5/Dxz72\nMVVZj8ejPV2FGp8Lyxc04+Rl4gNaMDOIOa21QAxwuQqPwtM68EbiaYbz279yMoD04mkE4hcHGI2O\ngVKK5PsvIP7a/wIg8H34SrgWnpphyiVuH6hOaiaZRNNbK7Kfsx4ktz2F5LanAMYF98JT4Dv9CxBC\nfVJNoMlYRj1FI2V/uezsDpx41Azc/od38P7+UcVhinmuAZxz7CycdObJpqrsfK8LeAlwuz3wp3y2\nav3uDMEk9lofklt3AQCYhhn47Hlz8NnzFsvH19/9T/SPRvH1S47GivZmU20/8+YBPPRcJ1ob0oSz\nrY0BkAEKSgFh5FDGOSzD4NufUQte197xIiIKAc8MrAoosRPszCWOmL0IYeA76VPwHHUW4m9sROKd\nx5DsfFU8ZqM/20RMTMCej1lRmg+WLyiMD24yIKXQUnKbOYFiM6RUUb7QClZ6aG5uxpIlS7Bp0yas\nW7cOmzZtwpIlS9DUpH73zjvvPGzcuBFr1qzB6Ogonn32WWzYsCHnse7ubrS3izl/h4eH8frrr2PN\nmjU5z8uGKSGUTUxMIBwOyw/g73//O+rr69HQoPZ/CQaDGVJyYbDGa0NZSzSPhVYJaTERhvcjse1J\n8Ae2gZ25BL4zvgimVl9oyBaxKTv6p7ikAuu+DWH4AGh0DMnOV8Adel9sK9Qn8hhxSYBPFtTvYkAp\ncH3wb0AngDNPNXUOSdEgCDncJInbK28zwemZbaeeWkFrDlH/YEBBKQU/0pPtjKIwHk2id2hyff7M\nILD2BkfbY4Kt8J99DWKv/B7JHc8CsDfvpfzBlce0EYk5/14VDY9P9NfjkypeOzvB8QL29VZpOKoo\nDjfddBPWr1+Pu+66C8FgELfddhsA4KqrrsJ1112H5cuXY926ddiyZYssUF1zzTWYM0fUfBsde/jh\nh/HKK6/A5XKBUorLL78cp556as7zsmFKCGXRaBRf+9rXEI1GwTAM6uvrcffdd1vytUYpEBDG5ETd\ndkGpKcsHxCNqX+KvPQywHnhPuRzupavTef70zvHVgkZGQQUORBFBSmMTopkytY9tOQJsyxEAAGFs\nANyeN8XtcB+Y4HSRzZuzfvGwxVGZpqMvjSAHOCgJdPXKFdsdwoAQ8VqFUbWmLNuwzfe+KLVkTmsv\npgKUmSjsdPSPxaU0XuYRjesH7pQyiEs0s1MubvjuWIkP+sZk14YqqigU7e3t2LhxY8Z+pQsUy7K4\n+eabdc83OmbEBGF0XjZMCaGspaUFf/zjH22r/7LwfZh4uA61n/0FABU7RnFQRNUVLJR5awBvDZiG\nNvjP+KIujUNGs6lIxNjzv4Z/9ZfS+w0ET4mkllIKGuoDO+8YCBMjjmvKBIGq0g6ZFTZIiptKyCVO\npTRlTKBR93AxgZBqRVkq+jJlviT100Fls7A1qJp1jKEkr7VTKIvKmjLzgycSn3qaMlnLnIwBDuUR\n7T6o/PBwpMkqqphUTBlKDLuhZKq2Qt9AiJoQIVqoUObyoPay/0LgX/7TlEAGiD5lAMB1/VPeR7l4\nKsVSlsXJEwAoL2rYYmMgwemg4X5we98qqN/mOpq5K5rgVPctueM5cD07IYwNgArZv5gJ8tOUkZoc\n1A9FDgJGSKKeiYImY6DjQ3B3nGKi6vxMp13KBauwbpY1SE3aZ8Re82WK1zCfcwp0ZwAmUThJzSs0\nqZ+azQ5UPzyqqDRMCU2Znbg4+UjGPi8k53brzEHFTMIkz0TKWm2YEB7AxP9+AwDAzlqa5RzRz0wY\n2AdASqxNZaZ8K6FNwK3UhEViHLyedIBF/NXfpwsyLEhtC5jgNDElTN00kGCr+DvlQ5dLKJMCF5ga\nYwfrQvijlNJUy/AWcaP3PbG9JvMUKWYWXY4XsLe3SuRpBKZB8REj5eS0AdIHVz7CUqEfaZMJlabM\nAVBKq0JZFRWHihfK5lC1vw8/3INzY38rul4CqD6dnZyEiWYBir30gOJYFvNl6hy+vxuAJJQ5j2ic\ng9fNYlzwopaJo+aTt0MYGxQ1ZeF+COEBCGMDSHa/rkqgLIU88DCOmJWiAEmWVEKWZ1va9zoAgG00\nDoPOFwf6x5HgBMxsqcGhQeOMD5UKpcnSyAezGAhCYQOmUHeGSYVb9G+lyRgol0Ds5d+BaWiDd+X5\ntjQ3HI5jdDyB6U0B9A2XfkBLFVVYgYoXypTgD3ci8vgP0juKUZQZUGJoYbWTNlE4OAMA37MjfSyr\nUCYuYPyAyKXGBFvhWflRJLY+ZVm/MtrUucGRGIf6Wi/GBD8STe2oC7ZmJFaXQOMTEMYGIIT70b9/\nP17fsg8HyTTdshLcR50NYXwInmXnZCmhSbuVB/ROIQfeAVgXSF0r3l10DZ5+Yx++ZYEGVtIgHDmr\nXhTKpMSeVcggBaZJywexRPq9pnkYMIvRnE8WJE0ZjYQQffIO8IfeAztjIWCTUJYe48GqUFZFxaAq\nlCkQ+cv31TuK1JpIp3O8gEQyuz+U1eupqy2V49HlReSJn6iOZfOtkbQKfF83SE2TGF3l8gICB8pz\nIKwzQyUS5wBKQQhV5a7UA/HWgPXWgG2Zh3FXBza9thnBgHE/GX8Q/jOuynpcemZWPg+moQ2EYRDx\nT0cPnz28Px8tXXdPCE1BL5rqvLkLV2EbVMJVuZsvU/6YsefvBQgDUtOUMy9vMejqCcHjZjBnWuml\nyKqiCrtQdfRXwHvqZ8FMP9KSupSLes4J2AbHXd8ZV8E1ayloXP2FmUtTBi4umy5lHxKd/Jh2IRoX\nHf0ZCKA2mZzMoFihrHPxlfI20zBLXXeOys0oTTsPhsQcq1Xt2KRCqQE3+xpTSqem+ZJNs+n713wV\nrtnLbBXKuntCWNAWBKNl4q6iijJGVVOmgGfpavAHtsEqVhzJoT3XBExtsD25F54C90Ix4m/svivl\nhN3ZqAGU+2VS1dSXMeUStnK4KR3/IzEOlAIMcmvKlLDK/FuQT5lO0+N18+RtJuVPlqtus00Ph2MY\nGYujfVZ92sm8ar/Uhf+j37SXf1ChKdMGsGRDLMFPSXoHEmiAe/GH4eo4Ga62ReB6d2V89FmFeILH\n/r5xfOTEuVX+vSoqClVNmRaWTeDpiSSXpszuCbrmEz9M/8imKVOQQcqaMmkfl7ClX3pzbTTOAYKA\nFnYcNRMHbWnXFApYCJSnEIZFkoqvF9M4K8sZhUHpT6aFWcGgUuCatVQmSLYDhZghp6LpEgAIw8B3\n+hdk9wjirQH4BKgN88O+w2EIlKrGeHVsV1EJqAplGhBWmfDWmi+0XE69ds81TLBVFsbMaA1Ifcqx\nPiWUWT3pGl1vJM4BMVHo8MUGLW03HxT75AkhiFNxLOUbeZlrPHQdTPnatNZm9LO6bDkLlfnS5M0v\n5JxShDSX6KV0KxbSh0e7zodHFVWUMypeKItBNNF5jvkXcYdC3VHMfKnUmuT+MnZgZpbMlyZINJmg\nyO+U1pQ551MWiXOT6ktmFQgB4tQNyrhAskSPFoqulK+Ni03fp6m8uE9lqMyXBZyTD0rNikc8Io2O\nHX5l3T1htDUHUOt35y5cRRVlhKm/+hWJCQSw190B73EXiTssnPmkhXKyNWUixOvScpjpgQmmaCUU\nPmVmwQ/uAz+0P//upRCNcfINGWg9oeB6CoVkIilkGGh9X+LUBVrXCsKwmnLZGs9xHGlfm6wahKpw\n5ihUH1wmX+Qp6eSvA1lTZrFfmUQa2z6zqiWrovJQdfQHtM5Aiu0iqlRs5/QpK7wZ0/AsX4PEO48Z\nmi9JoAE0MipryOQQ+GfvAjx+kRaDdQGMO7XtFjm4mPQ2t/tlAEDd1Q9kbcfoeiNxTk6nFK2xlnDV\nDKx6FoQA7yTmY838dDRvPjxW2bC3V+Nro5HgrGijCvOIxDm4XQySnGD6zk9VnzIt5LkkPm5pvX0j\nUYxHkzhytlooq47sKioBVaFMCxtMZ5E4BwKDScWB2cZz7IXwHPMvGVobJQIX3wIkovJvpnk2PCvX\ngkbDoHxSTLnEc+I2z4FycSA+DsqLXGaFJi9XKhgicQ6U8qlfk2evKSTii6i2CZ6JLcfqI0/UKadf\ntxmBKpevTdWM6SwiMQ4Bnwuh8YTp93gqEsfqQYrYljRllEtAGD4ItnVBUfV2V/3JqqhgVIUyw5nU\nGlVZJM7B52URjfO6RQUHVlJCCECMUxAxvjrAV5c+h3HBe/zFptuglGL83i/k1yeon0A0JkZfAgBl\n8qHEMF3UGJapynSqs6Durp5Q1demhBCJcwh4RaHMtE9ZmWnKaEpTFnv5QXCdr6D2M7/ISr1jBl09\nIQS8LrQ125evtIoqShUV71OmhWfZ2ZbVJfknRWPixF3uIITA1XGyHLVZCCJxDkQSUifB4b8ouUlp\nBZeEMj2BO4dPWTYIlKK7J6SiCSgx3++KQzTOocYnCshmKRvKxXwJjx8AAY1PgOvZKbouUAphYqSo\nart6QlgwKwgm9RKVWoBDFVXYiapQpgFTNw3jJKWWL6IepYkqEufgNxDKysnkxNRNA7gEqKCvFQSQ\n9YIZQkRGf9l8OYmM/kUuBNLzt/LZ9g1HMBHjsvCTqf9X4Qwk82W+5xSDUvEbJIQBvAHQiRHEXnoA\nYFMUOtFQwXVGYhwODUzojvESuewqqrAVVaEMgFbfIFh0W6Q5JBrPpSkrn9mG+EXzJ42JJg2udxeS\n+942dW7A5wIvUCSSqUUrH/OlVTqjAqQa3UTkOubLYp9y58EUaexsI1+b8hlLUwGReFJ+twvhKcsH\nlo1xC0G8tUjufhU03A/vKZ8GICYsLxR7ekOg0CdGrqKKSkDFC2V60xyfw/cq34ojsRyasuJbKxkQ\nXxAAQGNi4u3o4z9E7Olf6JeVNlKrmbS4xWJiwACdhOGZTkhun5BXaM1dPSHU+FyY3pT2tdFq9Kqa\nMmcRiXHw+1zGgTwaROP5p2YqVRBvDUB5uBedBne7SGEjFCGUdR0MgRBgflvQqi5WUcWUQsULZUDm\nZMpDEsosYvSPG5s4pvi8rIIckZUSyiRQITfJpnSPYvFUFOdkJiIuhKdMua0TxJBrAc41DLp7Qmif\nVS/72uidXUZDqeRBKUU0zosfEwQwe/fLJfoSAJiaBhB/EN4TLgVx+wC3DzQyWnB93T0hzJ5Wq/qI\nLT39YBVV2IeqUKYDK8yXWp4yY5+y8llK0+ZLtVCWfO+FrOdIV18jCWUxiazWAo1lnrDqUaS1gDrH\nClhlxqNJ9A5F0GFouqzCScSTPARKEfC5QEDyMl963OUx9XpP/SwCF35H/hgj/nrZp0yYGEHkbz8F\n17vLVF2CQNF9KJzVdFk+s2QVVWRHecwMRSHzVRdSwgChBs7qZmqmosCVU1NWVCulBZKi1OAPbANN\nxuT9wuAHWc+Rrt+fimKLJwrQlElFLQrVKqyW9Fl6PmW5YLSodxskIVdXkkeDVRQFSePl97ryGna5\nPtKmEphAgxjcI/+uB42EIIwNIvL4D8Ef2ArepFB2aHACsQRf9SeroqJRHjNDsdDMqLJPGV+4mUGq\nMpbgQSkQ8BrwSpXRQkr89XC1n4DkrpfA7dss7xfGFcnFEzFcWfsPsPG5ANJ5ISVNWSKRuu+FUGIU\nqeqStZYFpVlS/VLXh8xj+aCrJwSGEMzL4msjR1+W02AqcUgO+3k7+sc4TGvwiYSzZQbiD4LbtxmR\nx38ImogCIIDJNG1pYuSqP1kVlYuK15TpOvpbpSkDlZ16/d7sprhyWkYJIfCf9RUELvwOmOntyiPy\nlufA61jhOYDm/c+JO1I3QFrcJE3ZZDj6SyjW0T/NU5bel3vRzl6g62AIc6fXwuvW5tHUpFkqp8FU\n4pDe7YBP1JSZEYiTHA+OF2TNeUGPq4SfMfEHAcoDyTgCa28A3F7TuXO7ekIIBtyY1uDXVFr1Kqui\nclDxQpkehJSmjAjFOOSKE4n8Ne0z0pSV8CxbINjWdgTOux6Bj98i7lDSW6Q0YESzukgLVbwAR3+5\n5CRO4CTLdka5PLvI8QL29mb3tQFKep0uW0jmS1ELTkw9BPU55Qd21lIwjbPhv+BbYFuOEJ3/k9Hc\nJyIdyKL90CACh1nsUFnOk1VUoUXVfAkK7RLKp2RVYkSAarLqtN9Jdk2ZUMZzDds8B+yso0DjEzpH\n1RfucbFgGYKE5FNWCDWJRRN30bKd7FNWfH8O9I8jwQk5+MmqcBqRDE2Z+XOM5oOsmAIKI/f84+Ce\nf5z8m9Q2QRgfznleOJJA30gUpx89M+PYvL1/xr8HtwLJNQC8Vna3iipKDlVNmQ4kR3/Q4n3KZBNH\nmX4ZmwHx1gCppMXc4d3wb3k4dUSzjBHRaboon7ISgR6jf6HiWZeBk792na4qE5yDytEfMKcpS80H\nNUaa8zICU9sCYWwwZ7lsSciTe95E8/AWsIQCisChKqooV1Q1ZTrgLTFfinO0mS/jcqLE0APxBiCM\nDyH+1iMQQn3p/VTi1hL/E4hah0RS0pTlIZRZFH1ZyKPQmlsK7Ua2trt7QmgKetEU9BmcnLFRhc1I\nf3CxKetl7nsfVQhylQCmrgXcvrdBqSCmZcqCrp4QWIZg3ow6eZ8QCSH+8oOghAGhAiAknehyFVVM\nKipjZsgB7VS6w7MCi5M7EW1eWHCd0pocNeNTVuZwLz4DQqgPiXf+AuXdZri0r8lJ3t1oCAMBbx24\naFKkKGMKGJ7FRl+m/hck2+kkJBfy8/TXRefBkGl+sqpI5hwicQ5uFwO3izXNU6Y0eVYCSF0LIPCg\nkRBITWPWct09YRwxow6eVCALpRTxlx4ATUbR23YmZh56znTAQBVVaLF3716sX78eo6OjaGhowG23\n3YZ58+apyvA8j1tvvRUvvfQSCCG4+uqrcckll+Q89qtf/QpPPPEEGIaB2+3G9ddfj9NOOw0AsH79\nerz66qtobBTH/nnnnYevfOUrhn2tjJkhTwyx0/G14c/i33zZJxFToFTh2GukKSuumVIHO20eAmtv\ngDAxgsS2p5Dc+iQAoG5wO2Iv3o8aYQE+WfMa0PUa3q75HLhwSiibFPOlpLWzzKks80geVQ+HYxgZ\ni2eYdbTNSCj3sVRKiMQUOW1NPlMtjUa5g6ltBgDQ8SEgi1AmBbKcsXJWel/nK+A+2AzviZ/E2IhI\nTItY2Pb+VlGe+O53v4vLLrsM69atw2OPPYYbb7wRDz74oKrM448/jv379+Ppp5/G6OgoLrzwQpx0\n0kmYPXu24bH6f8ILAAAgAElEQVQVK1bgiiuugN/vx/vvv4/LL78cL7/8Mnw+0bJx9dVX4/LLLzfd\n16nrtGMRiI6jv0UVAxA1ZS5W/JrOhkrhlmJqGuE9/mL5d6x2JpK7XsGV8QfkfZ+ceBCX+UT2f5pH\nQnIZkxp9aUweW8hTNvInU6JSxlApIaIggSUwJxBL5suK0ZR5xDytNBHJWuZA/ziSikAWYXwIsVc2\ngJ2xEO5la5D0iCZNEi08p2YVlYuhoSHs3LkTa9euBQCsXbsWO3fuxPCwOgDliSeewCWXXAKGYdDU\n1ISzzz4bTz75ZM5jp512Gvx+kcZl0aJFoJRidLTwVGMVJZQJ4X78vOlBtLv6che2AJJPmZGWDKgs\n7QZRmCQHFlyAmktulX8fnn4qdtceryhtPkJNFoiKJo8t6nQZhmmW8vgI6DoYgsfNYE5rrSX9qsI6\nRBWZOszylEXiHBhCZDNdIZhS04VHXKyogZN+18GUk//MICgVEHvhfoAK8J3xRRCGQdKTGvtVoawK\nDXp7e3Hw4EHVXzgczigzffp0sKz4zrEsi9bWVvT29maUmzkzHf3b1taGw4cP5zymxKOPPoq5c+di\nxowZ8r7f/OY3uOCCC/Cv//qv6O7uzn1RtIIQ3vJ32n3rRbTvsTvlfR/84suq31XYj+5bL6Ldt15E\nI/u2UUop3XfHFbT71ovo6Bt/pZRS+sEvv0y7b72IJoZ7J7ObVVRRRZFIjPbR7lsvoqHNz5gqP/rm\nE2L5t5+S9wmCQPf86JN08JkH7OpmFVMUZ555Jl24cKHq78471ev5tm3b6Pnnn6/a95GPfIRu375d\ntW/t2rV0y5Yt8u977rmH3nLLLTmPSXj99dfphz/8Ydrd3S3vO3z4MOV5nlJK6SOPPELPOOMMynGc\n4TVVhg4dwMjIBKIRAQDw0lt7cfpJYsLswVAUQ7v7sXIgnUD7v/53M3bsG8G/XXo0ls1vLqi9H/zu\nbbhdDFiGYCKWxHc+9yFc8aO/AwDuX78aAOTfP/rSiWhtDBR8bVMNfO0MsOOH8eb2XiwJHIGBCNAE\n4J1d/dhzcCtmDTBY6AZefGs/VqysMVXnnkNh3PrgWwjWePCzr55acN++8tMXEE/wuOkLH8Lc6XW5\nTwDw3NsHseGZ3Zg3ow43fv5DAIAde4fxXw+/i/WfPgYL5zQAAP7y8l48+vJe3PfNM8HoEONedfs/\nwAsUbc0BfP+qExFP8LjmjhfxkRPn4uMfbs8oDwBPvr4ff/xHF351/enwe10IRxL4+p0vA0iPsyrs\nwbfueQ1HTK/Fl9ctw7V3vIiTls3Ap88xDg665y87sOdQGJ86uwM//7+tWNHejK9fcrSp9hJJHl/+\nrxdw8RntOP/EI6y4BNtBE6Jeb2xwGLG+USARRXLPm3C3Hy9S5QD4j7tewZGz6nH1GS2YeO5BsHOW\nIzb7RMRTc/Lz7/agPcGCHxiCoJinq6hcuFwMGhtrsGHDBvC8mk80GFSn6Wpra0NfXx94ngfLsuB5\nHv39/Whra8sod+jQIaxYsQKAWjtmdAwANm/ejG984xu46667sGDBAnn/9OnT5e0LL7wQP/zhD3H4\n8GHMmpX2n9SiosyXhBUjIN0k/RAJkN0PqRg7QapK0XxpLPtOKXOEBRD8opBCeDGaagyS8MPA73Ph\ngfHT8Yfxk8EFWkzXKT/CYu2PVpkvDSn9szStaXtvbxgCpfklaK60wTSJUCYWJwSmecoqhQ4DAOD2\nA4wL8Tc2Yvy+KzH+4LWIv/xbxF8TuQqHwzEMh+Non1mH6PP3AYwLvtOvyKCZiQheIKFHQF1FJUIi\nJG5ra8Ps2bNVf1qhrLm5GUuWLMGmTZsAAJs2bcKSJUvQ1NSkKnfeeedh48aNEAQBw8PDePbZZ3Hu\nuefmPLZ161Zcf/31uPPOO3HUUUep6uzrS7tKvfTSS2AYRiWo6aGCZgcArkyhrOjwuByIxjljfqks\nXShnCAExCssdE50h48QLUIASgoDXhQnqw+uJI3G8USU2QeZMK2AMKE9Jy4jph5vvY+7KQqhZRWlA\nFX0J8z5lSif/cvcnJYTAe8rlEEKHQdx+JN5+BABAuTgAoPuQ6P+zPPY2hL4u+M68GowmSpMAiFAv\niEGwQBWVhcjTv0DzlbebLn/TTTdh/fr1uOuuuxAMBnHbbbcBAK666ipcd911WL58OdatW4ctW7Zg\nzZo1AIBrrrkGc+bMAQDDYzfffDNisRhuvPFGub3bb78dixYtwg033IChoSEQQlBbW4v//u//hstl\nLHZVmFDmAQC4oSaFtWNeFKOxaGriNnbqFcp9ZtYg2nEeerq6gGmiKjgO8bkwEIqnCrBIoC66FpLJ\n6J+rbu2i3tUTQltzALV+8xx3lTWSJg/axOKEaDO56iMa41DfVDmuCgDgWXKG6nfi7UcAVnznuw6G\nMNcTQs2uJ+CafxxcR56kW0eEegDdVG1VVCL00/ZlR3t7OzZu3Jix/95775W3WZbFzTffrHu+0bE/\n/elPWdt94IEH8uonUGnmy1Tk3wL3gHq/HW2l/kfjXEWnWNKDEGjCz8c+AsEj+pSEiKhu9sUG1VQB\nk5nrr8i2ZU2ZYh81KXwTQiBQiu6eUH6mS7GR/MpXURDS/IOK8Vo1X+aE55h/AdM0B3RCND/tOTiM\nzwZfAfEG4D31s1k11BHqqZovqwCQmkeLzUtdwqgoocxpcDxFghNyJh+u2HU0Nf+GIAplLi6qWrCK\nJ3AtAFb7lOk83FymUUop+oYjmIhxOYUybVWVOpScRjp9mpISw9x5UgLzQmFWuC9FEELANM2CEO5D\nIsljcfgVTBMG4T3t82D8waznKX3KKKXgB/aBJqJZy1dRxqDlK5ABlWa+VEybVOBkzRm1hTyWIBoT\nc7XlSrE0dadYa9DFLMCLE4vQOOdcLPROrqZMTrNU0NmZZxX6bDtT3E1Hmk2vVOmDyGFo0yWJCcmN\nHwIvCIgn+IJN9JPIi2wpiLcWNB5Bz/s7cJZ3G8IzjkPdvGOylycEw0ItCJ8E17MTia1Pgj+wFZ7j\nPgbvMesc7HkVJQG+vIWyytWUJbKTGUoodp2bSAlluTRllb6iCoTFnyIngPcE4fcpNWXmYfmCVWSF\nkjYsryerKNzVE0KNz4XpOfyPtL2s8KHkGGRmfsk1wYRPWTQuLib+PIMDyg3E7QMSEQTeeRBhwY+6\n03OnoBkRRFeH6F9vB394NwBAGOk1OqWKcoXA5S4zhVFZQpkyEi6l+tabSq1Y3wmUee6qmjIjyNdP\nAL9HIZRNhqasiIehir6UEwwUVqHkT8aUi3qkzCCbL33m0yxVWjLyrEgFXAXig/gbcyaCDQ05TxkS\nakFB4DryJNRc+iMAANf9mq3drKI0QcvYnwyoOPNlGjRpvz9CIimS1VZ9ytSQaSc04i8B0ZCqToZA\nIvWtOKTTPilqzvGcpcMTMQ7hiQROXjbDsLze2VPZ32gqISOxuGi/NDwnqhccUIGgEZEK5y1uIVxz\nj8pRWkQv3wjukjsRbDRH6FxF+ULo3zPZXbAVlaUpU0DtJGr94q9UcOT0Kav0hTR1+aWgFKJF9IXo\n/NA+WTPVhidEUl1TkZelcNMqEFoBKx9NmWi+rNzn5lpwPPiZK/Bw+Fi0m/SZBACw6XnUc/T5AGGr\nzv4VCL6vc7K7YCsqVijjD2xF9Nm7UIsIBGLvbcjpU1aFLvKRNyYlUtMAesGX+YjeDCGY15Y9Gq2K\nyUUkzoFlCDxuce4ww1Mm02hUuPnSNXMxds77FBJw48iZhREjM9PmA5SHMDZoce+qKHXQeHmTCFeW\nUKZYIRPv/hV8z068jaOwo+YEW5vN6VNWqYoyjRwlCWGSH1VpiVkmoGt5pdnLGGDu9Fp43eaFeamV\nih1LDiMSE/nGVPQmOTVlqWjsCjdfAkBXTxh+L4uZLeZy22pBUvQZNCpGKVMuAX5ov2X9q6J0wQ/u\nAzOjY7K7YRumhFA2MjKCq666Cueeey4uuOACXHvttRgeHi64vq7kdPjOvgY1l9+BZ3AqIqy+RsKK\nBY4A8FU1ZSpo76s2Ak3WLE6io38haZaUkATLQseQWdLYjOjLig8bcQZRTU5bkafMnE+Zv8I1ZYAY\nyLJgZr3Gh9Q8GFkoE9M0xV74NSJ/ujFvpvcqphYon4QwtB+upjmT3RXbMCWEMkIIvvjFL+Kpp57C\n448/jjlz5uAnP/lJ3vVIU+bT0eVwL/iQnKA8YwG2QBiQ6vR5XTkj6CotzVI2SCZImZCzkAdh0b20\nio5D3R3zfTPLT5bZYGGnVZEfInEuk77FrE+ZJ78sAGlMOd2xLqJxDgcHxgv+8ACUmjJRKOP2bRZ/\nV4WysoYw3AMIPNgyFsqmxCdbQ0MDTjghbWJcuXIlHnrooaLqlJI9Jzl7w2tz5b0E0gt3khPQPxrF\nzOZA0ZoaJTheQCTOIRjwyPsGRqPgBYrxaFLeN6MpvzyLheLwkMYnQMPY6vO4VL+dRDECslKIlB4f\nxwvy//c/GDUtM+abXqnrYAg1fjdC4/GMY7EEh56BCdX673Ex8HlYtDaKPGjxJI++4Qhmt9aapuGI\nJ3kkOUEeM5RS9I9E0drol8fvRCwJF8vIplheEDAwGsMMG/I/Ukqxv28cydQ9B4A5rcZm4OFwDMNj\nmffMCARAaDyh8Q0jCEeS8rzCMgRHTK+TNUHSM/B7WTAMsSw+I5HkcaB/HNMa/ar3W4nQeByhiQTm\ntNZaOq+YQWgigVq/CyyT/v7f0xsGpUD7rCJ8Jj0BgHGBO/QeuA/eBXgxOIbGxoFga7HdLgnEEhx4\ngaImR6CYFokkj7FIEs31Psv6wgsC+keimNGU39p0eDiCWILD3Ol1ltD78AN7AQCJurai6ypVTAmh\nTAlBEPDQQw9h9erVGcfC4TDC4bBqn8fjQWur+JIOjkQgeTD84Hdvy2W8brXCsGN2A7bvGUZT0Ftw\nP8ciqUnCRNm+kQgWzAxiwzO78eKWQ7j5iuMxp7W24La1+PVf38PrO/tw3w1ngiEEI2Nx3HD3PzPK\nLZxdj/WXH2tZu3roH4ng3k07AaRlrpYGP/b3j8OXWjwl82VBr7BFi44nD38uPUiC5c4PRnDc4lY8\n9cZ+ecHOhmMWTsM7u8W8rE1BkxNq6nJ//n9bMw4lkjw8bha/emQ7duzVN/f//LpTURfw4HdP7cKr\n2w/juotXYOWRLaaa3vD0bvQMTuA7nzsOAPDGe/34n7/swHUfX4GVHWIdP/nfd9Exux6Xnb0QAPD4\nK/vwl1f2WT7GAWDbnmH8bOMW1b6zjp2NT5+zMOs533vgTYQjyazHjXDMwmnyts/LYtueIWzbMyTv\nu+L8JTh1hbh4/PLP27Bz3wga68Q5RVpo57UVR/Hwpxf24Jm3DqB9ZhDf/uxxumVuvP8NjEWS+I9P\nrsTSeU1FtZcPkhyP/7znn7h0dQdOP3qmvH9/3xgAYH6egSzKDxpCCIg/CH7/FhB/EK4Fx4Pb8wZo\nfNySvpcC7n18J6JxDt+8LHu2Az3ct2kn3to1IM/3VuAvL+/D46/uw42fPw7zZph7bqHxOP7zHpFL\nTvkuFANhcC+ItxYPPH8Y326fV3R9pYgpJ5TdcsstCAQCuPzyTBbo3/72t/jlL3+p2nfMMcfIWrWx\naAI1AI5d3IqPLj1aLjN/pnqQffSkI3D84tacbOpGiCVEDdyZq2bJ++782mkqTczXLl6Bn//fVrhZ\nUSiUJvRo3FrG4jfe6wOQot4gBMNj6WwGq4+ZhZVHtuCxl/cimrCflE9iNVfiC+cvxjnHzZZNdoV8\nzVutAJAWz/w6kd6UNEF+jyjcmdHGXPnRJThz1Sy0Nvrzbrop6MXnz1sMAHjkpT3Y2zsGjhfgcbOy\nQHb8klacurwNnQdDePzVfQCAeIJHXQDoGxa1l/mMvZ0fDINV+AR1HhT5pwZCaZqCwdEo2prT79HW\nbnGMxxLWs3JLjvSf/8hiNNV5cd+mnTmvJxLn8KHFrTgtjwXj7sd2IBLnoHSH+trHV+CwdA8TPP77\n0e2qtnfuGwGQ9jWc31aH73zuOMyeVpxgOpJ6lyMG1zmWEjqtnldyYTzKIRrnVdp4AOB5cQ7MJ5BF\nD94PfRw0PgH3kg+Djg+LQlmsPISyaJzD1u4htDXnHwjx1i7xw04QKBjWmolxe2oOienM39kQU6wn\nb+3qt0Qo4wf2gZk2D5FI+bL6Tymh7LbbbsMHH3yAu+++GwyT6Q73uc99Dh/72MdU+zyetEpfGiTL\nFjRjxoLmrO0whBQlkAHpr7qmurTGQ2salNTLkpgmLVRW85YREFBQuU+SwzEAzGypwbIFzfjH5h4M\nhnKnnioWes7QNT43Fs1tzNg/1T1o3C5GvlozC6Lf68JR8wvTZNT63ViWGtO9QxHs7R3LuNOtjX4s\nW9CMeDJt3st4GiaH3nA4huFwHC0KE4kkcEsO8JRSUVhQ1OmEYNAxux5tzTVwu9jchL0UmNbgl++d\nGbhcDKCRsVsa/GhpEIVpSQjRa1r6eCCE5K0p0oORMAaIZicJTruu5uqbaWSZCNwLT5G3qU8UbsvF\np2zbniHwQuk4iBby3kq9rwu4sXPfCGIJLu2aUgAol4Aw3APP0SuAMg60nTJC2U9/+lNs374d99xz\nj0rQUiIYDCIYzD7RxVNCmc/jYDSkgWSR5rISh6+eFsmSLhCoVgjlZKnsntOTdlbtVjEMrpMIbW+V\nvyMxe4QRIv8nGTszn2eKakSHxSHfR69nipUmbun9iiX4jD5IZWwZa5rIWWLG+x5FDLMsJ5L0i11g\nxeYxkWNc2TWnmEHUpjGvB+IRNUrloil7t7O0+Nfk97aAc1d1tODFLb3YsXcYxy4q3N9PGD4AUF7k\nqCtjoWxKRF92dnbif/7nf9Df349PfvKTWLduHa655pq865E0Ub4i1ebmkErXYzThZzlo91yuEhAc\nFnzyubapJZIZwzKtQTZkymQZGleSWTTjgZil1Og6GMo4Pf01TVS/lTXaaSLXxItktK17DrX+FZDv\nv7XVylDd85ixkBuJFeYvZwUkc7JVMBqbhGEAT6AshDKOF2QzfzGwch0pRsPdMbsBNT4XNhcoaFKB\ngxDul5382WnzyjrIfEpoyjo6OrBr166i65E0ZSxrvyyaXiCyz/jZPqjtHnDZNGWlwqcg37spJpXp\ndlcyGdsslKl4ayWONG0ZXalMDbMTeVpTlj4hfY3ivkg80xyf5ISMfZZBy9NLMvdlnkKRr/if+zaq\nOeqU11rMmNY7N5ewr9SUOf1226UdzgbGH5TJZKcydh8YRSTOweNiUNxTs+6JJ1LvbT6SnjTuWYZg\nRXsLtnQNghcEVSSuGcRfexjJ7c8AEKlQSE0TKD2YVx1TCVNCU2YVJE2ZE2HhZixw0rGML0CbVGVU\nT0CYYoLPlILi3tpmvlSZ6qDepvqUL8oPhUJGWjzBY3/feMb5EY1p0ulFWX6PJL8t5E59BGr9K6B9\n5+PJ9DOwMh0YpVS+x9muU6kpczrHrlXaYbP3jNQ2QUglO5/K2Nw5CLeLQcechsnuijUgoglzIsah\n80D+QjO35015m2mZl5rzSkOBYAcqSiiTJ8dSU8FQeyfM9OWmNBg6i6XT/EWpVo2PltpzygWd/lKN\n1sjGxjO2lIs2oLifBvY9M8Nw3+EwBEpRF3Crysc0pkmja7bTh1nWZJHc7xVF4dOB2fPU7gKFtaWH\nWILPyatn/7gzaNthoZwEGkHHC8/0UgqglOLdzgEcNa+p6OhUB5TRprFsQRNcLFOQCdO98FR5m502\nv7iOTAFUllBmQxh+LhgJFozCzKRc0Oz2Rcnq6G9Tu3p9MCxTQL2TKcBla1n6wucFQTadO9Oh9LjS\ne9ZGz9yMT5lkutQS3Gp9yAwdvW1YMeQq0yGOOcoX2IccQ02rAc/uLlAcVBrvLNeibHvKRl9KyNF/\nJlAvM/xPVRzoH8dQOC7z/BUDG2Np8gIBgc/jwtJ5jdjcOZD/e+dJUwSx0+YV0IOphYoSyrRf8k7A\ncBKWo+SoaoK1/mVS+xjpaU+cEmkqMTejExFwSs4sWSig+loaVfRlaoLMZ57sOhhCW3MANX63aoKV\nQvilfVpzpqBQj9k5CpTCp9F1pWU4a0e/bG6z2Ywr1csa5I9UCsZOv3tOa8rg9gECJ+ZHHBsAt38L\nKD95gQ6F4J3dAyAAjjZJ4GwIix635AdaLFZ1tGAwFEPPQJ60JTTdPtMyz5K+lDIqSihT+nbYDTNf\nA0pHf9UEZvHcqfQxAgyiokqGEsPRblgGvcuh1CETUpboS30tjZGnv3EzlFJ09YTQPqseuTw7tJF/\n0YSdHx6ZApiYJNzohFQ5qzuilslse/5SvTUGCc4n1XxplU+ZyQdEUhqViT/8OyYe+gaiT94BrvsN\nS/rgFN7tHET7rHrU14i0T6UwFao1subPoxSoI1GAiuvuyiNbQAC80zmQXwdSQpln1QVgahrlussV\nlSWUyZoyBxz9pZbMePpDY2aw6VWUzSkxe8wpJjthGvkoMErS+yzVKSf5mgD1mJtQCkY6QQH5Rv4e\nHo5gIsbhyFn1qnqUvk1aM7mehtaOIS6NbyPKD73y+Q6etM9aFp4yuWmxfqufv3RF0rMN+NzZHf0L\nXFCtQNRhOg62tR1M0xywM5fCe/LlACEQQocd7UMxGAxFsb9/HKsWilqyYuc0q9aRgiPHuThubdyI\nGXs2AQDqa71YMDOYv19ZigDZc9xF8q5ytrhUqFDmAMxEX0pF7daUaX7rRl+WkFQzVV+4bM/aTm2F\nkiFeC0pzCwT53mm1P1laFRVTUS9ohH+a+TFg6zNW3BOjVmhhMpm2mcz9mgOq52+hqVS6nwEDTZna\nfOksrB73ufrPti5AzcW3wH/Wl+FZdjZITROEsdIiYTWCRBi7qmNajpLmYJU2qVCFAYmJc0VDX1pb\nubKjBR8cHsNwOI/sMVQAQNRz3NRcIkyhYoQyjhcgSClHHHQKN2opbVW026csVa+uo39x9Ah590Gx\nXUJyoC2Qrs8J3xo1T5n4n0LffGmkScpldu/uCaHG58KM5oDKPKgyieuMM6BwM4hpyEKWwk/SRDvW\nB4moecrsInCV7m/A58p6nZE4B7crNc077ejvtE+ZBkzdNAhjBTiWTxI2dw6irTkg580tFRSsMEjd\nd0ZIn3/MQlHgfLcrD2GZCgBRiypT44kWhilBHmsFHNWSQTFozElljkVJCZQioch9qMeSUIW1sJM4\nVu+5EYVQoA7qyDwpM/rSGF09YbTPqgdDUq3opAjTmiv1fKsciQzL4VNWaDYvXWoR1XF1fyZscheQ\ntGBSrlHdMnEOAZ8LofGE847+k+jPBgBMsBXJXS9i/L4rAJcXxOXR+e8R/7u9IC5v+rfiONs0G2xr\nu619jcSS2H1gFGuOn6M+UALSR8HPUcgMEGhrrsH0pgA27x7A6mNmm6qGCgKQJ+HsVEbFCGWxJI/J\nGOHGjP4K6gLV17Q99ktK1WYmLRz5oszHpyyfJaxEpUqnHP2V2h6lUKDXNilQKpuIJXFocAInLJ0u\nN5QWwPLTlNk51JQyk/GYtnm8a6JQrcZEjIPPw4JlSFaBKxLjUONzi0KZg9OfliNvMuBZtRYk2Apw\ncVAukf6fjIPyCYBLgMbGQJNxUC4u/ubE/Vqdfs1lPwFTaz5pfb7Y2i0mILfKdAlY944VbsVR+pkK\nIClt16qOFjzz5gFEYpyh6T19cqamrJxROUKZUlPmhPnShMOKsht2asqIQiU32V+vplACX4eFQGsG\nk346kX9Qd5iZII/N51Z394gcUBI/mZJyQs/vRJ7MdakhrH/ImQJYLp6yVKkC54NcZ0m9sSvQIxJP\n5lzUInEODXVeW9o3QoITZIoUq5BvbUywFd5Va/Nvh1KATwJcAtyBrYj94x4Io722CmXvdA4iWOPB\ngpnB9M6ilylr7r/KFzQv82VaU0bjEyC+OgCiUPbk6/uxbc9Q+gMvVz0KoawQ5cHevXuxfv16jI6O\noqGhAbfddhvmzZunKsPzPG699Va89NJLIITg6quvxiWXXJLz2K9+9Ss88cQTYBgGbrcb119/PU47\n7TQAQDQaxbe+9S3s2LEDLMvihhtuwJlnnmnY14oSyiZDmWKmTe1XpW0+Zcj080ibtJy5O6ov+lxN\nlqj2Kx+I/oI8/F7WHr4yneemJCXO6VOmgdHY6+oJgSEE89vqMirSm7i1Y80pE70y9ZQpnjLL21c3\nYBslRoxLmS5J1uuMxjnMbKmxpX0jTLaWrBgQQoCU6ZJpnAUAoMm4be0lOUEUUJZMl99dK2DVK1Zo\nYnnlXC/0dYPMWgri8qB9Zj3qAm5s7hwwL5QpzJeFXNd3v/tdXHbZZVi3bh0ee+wx3HjjjXjwwQdV\nZR5//HHs378fTz/9NEZHR3HhhRfipJNOwuzZsw2PrVixAldccQX8fj/ef/99XH755Xj55Zfh8/nw\n61//GrW1tXjmmWewb98+fPrTn8bTTz+Nmprs72TF6ARjKjb/0qDEUCaOttUJWmG+1Po3WZmLzyoU\ncvmTehVZn7G4PxJPwm/g92N5FxTPW5c8VlE046vTQIrp7glhTmstfB6XXI+WKFZZbwbDv8M+ZTnH\nRNFSWTZKjPR7DdggoMh+fBwCPnfWYkKKp07iMXPSfGmlIDqp2daYVKojap9P8vv7RxBP8FhlAYu/\nEpZFX2bRcFOBR2TTbeA+eBcAIEyMIPbCryFEQhkdiD71M4zffzXGfvMVRP64Hl8PPomjezYi8uID\niL/1CBI7nkNyz5vgDu+GMHoYNBFJz01C2vSp6YIpDA0NYefOnVi7VtSarl27Fjt37sTwsDol1xNP\nPIFLLrkEDMOgqakJZ599Np588smcx0477TT4/SJH3qJFi0ApxeiomIP1b3/7Gy699FIAwLx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E58dyhVCmXjhV9QHojEFEKZUGNLENYRM+rQUOvBuyb9yqqO/mUE2dHfAeQz309ozZc2fQlEEylu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\nAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VnPs4WWBfF5c",
        "colab_type": "text"
      },
      "source": [
        "### (d) plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L3PagxZneYRa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import plotnine as pn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brzOafuKfkMd",
        "colab_type": "code",
        "outputId": "728c71f9-b6ab-465f-e0e8-92b913a8b280",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        }
      },
      "source": [
        "(pn.ggplot(coEvents_std, pn.aes('numCoEvents', 'std'))\n",
        " +pn.geom_point()\n",
        " +pn.stat_smooth())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/plotnine/scales/scale.py:93: MatplotlibDeprecationWarning: \n",
            "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
            "  if cbook.iterable(self.breaks) and cbook.iterable(self.labels):\n",
            "/usr/local/lib/python3.6/dist-packages/statsmodels/nonparametric/smoothers_lowess.py:165: RuntimeWarning: invalid value encountered in greater_equal\n",
            "  res = _lowess(y, x, frac=frac, it=it, delta=delta)\n",
            "/usr/local/lib/python3.6/dist-packages/plotnine/stats/smoothers.py:146: UserWarning: Confidence intervals are not yet implementedfor lowess smoothings.\n",
            "  warnings.warn(\"Confidence intervals are not yet implemented\"\n",
            "/usr/local/lib/python3.6/dist-packages/plotnine/utils.py:553: MatplotlibDeprecationWarning: \n",
            "The iterable function was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use np.iterable instead.\n",
            "  return cbook.iterable(var) and not is_string(var)\n",
            "/usr/local/lib/python3.6/dist-packages/plotnine/layer.py:449: UserWarning: geom_smooth : Removed 991 rows containing missing values.\n",
            "  self.data = self.geom.handle_na(self.data)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CgYGBXkVPUVERgoKCvF4bFBSEoqIij1hhYaH7tYGBgR7DYFV/VkFBAT7++GO88sorv5vT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\nIAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8784560748159)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUk2jMtYfp8v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#overlap이 많이 됐을 때 수익률 분산이 큼\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WpG5w5bzgKdo",
        "colab_type": "text"
      },
      "source": [
        "# 4.2. uniqueness 구하기, ar(1) 측정"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IsYHQ2C6htWM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pt_sl=[1,1]\n",
        "target = DailyVol"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f7cfjQehhQmi",
        "colab_type": "code",
        "outputId": "28bec6b7-6338-4e74-985b-64127655b89d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "tripple_barrier_events = labeling.get_events(close, tEvents,pt_sl,target,min_ret=0.01,num_threads=1,vertical_barrier_times=False,side_prediction= None)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlfinlab/labeling/labeling.py:124: FutureWarning: \n",
            "Passing list-likes to .loc or [] with any missing label will raise\n",
            "KeyError in the future, you can use .reindex() as an alternative.\n",
            "\n",
            "See the documentation here:\n",
            "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
            "  target = target.loc[t_events]\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P5cqG2T1gSzC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tW = sampling.get_av_uniqueness_from_triple_barrier(tripple_barrier_events,close,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vSeHBSJpidNd",
        "colab_type": "code",
        "outputId": "91766c44-c5c6-47cc-a276-4eb4caec9763",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "tW"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tW</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-10-02 09:32:42</th>\n",
              "      <td>0.812500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-02 10:34:31</th>\n",
              "      <td>0.920000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-05 12:55:39</th>\n",
              "      <td>0.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-06 09:31:29</th>\n",
              "      <td>0.344048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-06 11:16:28</th>\n",
              "      <td>0.311538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-15 09:30:00</th>\n",
              "      <td>0.269271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-15 10:38:24</th>\n",
              "      <td>0.262644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-16 10:01:27</th>\n",
              "      <td>0.454610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-17 09:30:00</th>\n",
              "      <td>0.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-05 09:30:00</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1088 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                           tW\n",
              "2009-10-02 09:32:42  0.812500\n",
              "2009-10-02 10:34:31  0.920000\n",
              "2009-10-05 12:55:39  0.900000\n",
              "2009-10-06 09:31:29  0.344048\n",
              "2009-10-06 11:16:28  0.311538\n",
              "...                       ...\n",
              "2019-05-15 09:30:00  0.269271\n",
              "2019-05-15 10:38:24  0.262644\n",
              "2019-05-16 10:01:27  0.454610\n",
              "2019-05-17 09:30:00  0.333333\n",
              "2019-06-05 09:30:00  1.000000\n",
              "\n",
              "[1088 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tifh5KTgigtT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from statsmodels.tsa.ar_model import AR, ARResults"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JZkNBL8iyhO",
        "colab_type": "code",
        "outputId": "d0d94113-f378-44d0-963c-52d98c25e64c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "ar = AR(tW)\n",
        "modelar = ar.fit(1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tsa/base/tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
            "  ' ignored when e.g. forecasting.', ValueWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ifilmpwzne9F",
        "colab_type": "code",
        "outputId": "ad0ccd76-2ba4-4961-af3a-7d9dd12907c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "modelar.params"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "const    0.094741\n",
              "L1.tW    0.724575\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c7l4m8iejjBc",
        "colab_type": "code",
        "outputId": "d2a677bd-e2fc-438c-ab1e-73e306948771",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "print(modelar.pvalues,2*'\\n', modelar.tvalues)\n",
        "#yes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "const     1.213312e-27\n",
            "L1.tW    8.382844e-261\n",
            "dtype: float64 \n",
            "\n",
            " const    10.895326\n",
            "L1.tW    34.498724\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RZMaFrTvoaAh",
        "colab_type": "text"
      },
      "source": [
        "# 4.3 Fit a random forest to a financial dataset where $I^{−1}\\sum_{i=1}^{I} \\bar{u}_i \\ll 1$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3CNqfX4SgPLf",
        "colab_type": "text"
      },
      "source": [
        "## (a) out-of-bag mean accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fZLje9DxgEJ4",
        "colab_type": "code",
        "outputId": "6136cc32-86d8-4d2b-81ef-a08724d7d716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
        "\n",
        "Xy = (pd.DataFrame().assign(close=close,close_lag=close.shift(1))).dropna()\n",
        "## i'm not sure what this is. 저 수식을 만족시키는 건가?\n",
        "\n",
        "y = Xy.loc[:,'close'].values\n",
        "X = Xy.loc[:,'close_lag'].values.reshape(-1,1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25,shuffle=False)\n",
        "\n",
        "n_estimator = 50\n",
        "rf = RandomForestRegressor(max_depth=2, n_estimators=n_estimator,\n",
        "                           criterion='mse', oob_score=True,\n",
        "                           )\n",
        "rf.fit(X_train, y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=2,\n",
              "                      max_features='auto', max_leaf_nodes=None,\n",
              "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                      min_samples_leaf=1, min_samples_split=2,\n",
              "                      min_weight_fraction_leaf=0.0, n_estimators=50,\n",
              "                      n_jobs=None, oob_score=True, random_state=None, verbose=0,\n",
              "                      warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pcI7l7iap-8_",
        "colab_type": "code",
        "outputId": "7e03a38c-1ab3-42d8-e9f5-ce8333054637",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "rf.oob_score_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.929785556021754"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jimDpx9PwuDl",
        "colab_type": "text"
      },
      "source": [
        "- OOB Score가 뭘까?\n",
        "\n",
        "https://towardsdatascience.com/what-is-out-of-bag-oob-score-in-random-forest-a7fa23d710\n",
        "\n",
        "https://en.wikipedia.org/wiki/Out-of-bag_error\n",
        "\n",
        "- 왜 하라고 한 걸까?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHdCUSnpqN9Y",
        "colab_type": "text"
      },
      "source": [
        "## (b) k-fold cross-validation (no shuffle) mean accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdXBH8PZrF9M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "n_estimator = 50\n",
        "rf = RandomForestRegressor(max_depth=1, n_estimators=n_estimator,\n",
        "                           criterion='mse', oob_score=True,\n",
        "                           )\n",
        "\n",
        "scores = cross_validate(rf, X, y, cv=5, return_estimator=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y1rqjqhq53Az",
        "colab_type": "code",
        "outputId": "9330f86a-eb99-4bfb-8a2f-46a938083212",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "oob_scores = [est.oob_score_ for est in scores['estimator']]\n",
        "np.mean(oob_scores)\n",
        "#이 과정은 아직 이해가 가지 않아 베꼈다."
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7662238411646467"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XqCF2Of6Itp",
        "colab_type": "text"
      },
      "source": [
        "## (c) comparison"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfRu2ZMU6gI0",
        "colab_type": "text"
      },
      "source": [
        "Out of bag accuracy is higher than cross-validation b/c the incorrect assumption of IID draws leads to oversampling of redudant samples.\n",
        "\n",
        "For random forests this means that the trees too similar. The random sampling also means that in-bag and out-of-bag samples will be similar inflating the oob_score_. In this example the cross-validation is less-biased.\n",
        "\n",
        "from BlackArbsCEO"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-gAb8TL6eop",
        "colab_type": "text"
      },
      "source": [
        "# 4.4 exponential time decay factor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eWckaSo08VAx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#original one\n",
        "\n",
        "def getTimeDecay(tW,clfLastW=1.):\n",
        "# apply piecewise-linear decay to observed uniqueness (tW)\n",
        "# newest observation gets weight=1, oldest observation gets weight=clfLastW\n",
        "  clfW=tW.sort_index().cumsum()\n",
        "  if clfLastW>=0:\n",
        "    slope=(1.-clfLastW)/clfW.iloc[-1]\n",
        "  else:\n",
        "    slope=1./((clfLastW+1)*clfW.iloc[-1])\n",
        "\n",
        "  const=1.-slope*clfW.iloc[-1]\n",
        "  clfW=const+slope*clfW\n",
        "  clfW[clfW<0]=0\n",
        "  print (const,slope)\n",
        "  return clfW"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJZ7DbzP9AHn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getExTimeDecay(tW,clfLastW=1.,exponent=1):\n",
        "    # apply exponential decay to observed uniqueness (tW)\n",
        "    # newest observation gets weight=1, oldest observation gets weight=clfLastW\n",
        "    clfW=tW.sort_index().cumsum()\n",
        "    if clfLastW>=0: \n",
        "      slope=((1.-clfLastW)/clfW.iloc[-1])**exponent\n",
        "    else: \n",
        "      slope=(1./((clfLastW+1)*clfW.iloc[-1]))**exponent\n",
        "    const=1.-slope*clfW.iloc[-1]\n",
        "    clfW=const+slope*clfW\n",
        "    clfW[clfW<0]=0\n",
        "    print(const,slope)\n",
        "    return clfW"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SYySYXH8Z4d",
        "colab_type": "code",
        "outputId": "64104f70-55ce-4de1-8168-14c16f4df2fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "getTimeDecay(tW,1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tW    1.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tW</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-10-02 09:32:42</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-02 10:34:31</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-05 12:55:39</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-06 09:31:29</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-06 11:16:28</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-15 09:30:00</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-15 10:38:24</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-16 10:01:27</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-17 09:30:00</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-05 09:30:00</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1088 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      tW\n",
              "2009-10-02 09:32:42  1.0\n",
              "2009-10-02 10:34:31  1.0\n",
              "2009-10-05 12:55:39  1.0\n",
              "2009-10-06 09:31:29  1.0\n",
              "2009-10-06 11:16:28  1.0\n",
              "...                  ...\n",
              "2019-05-15 09:30:00  1.0\n",
              "2019-05-15 10:38:24  1.0\n",
              "2019-05-16 10:01:27  1.0\n",
              "2019-05-17 09:30:00  1.0\n",
              "2019-06-05 09:30:00  1.0\n",
              "\n",
              "[1088 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xaUNNB_9LS_",
        "colab_type": "code",
        "outputId": "3b9b40d6-5c09-4e60-df5f-2851fde913be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "getExTimeDecay(tW,1,1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tW    1.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tW</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2009-10-02 09:32:42</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-02 10:34:31</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-05 12:55:39</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-06 09:31:29</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-10-06 11:16:28</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-15 09:30:00</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-15 10:38:24</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-16 10:01:27</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-05-17 09:30:00</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019-06-05 09:30:00</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1088 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                      tW\n",
              "2009-10-02 09:32:42  1.0\n",
              "2009-10-02 10:34:31  1.0\n",
              "2009-10-05 12:55:39  1.0\n",
              "2009-10-06 09:31:29  1.0\n",
              "2009-10-06 11:16:28  1.0\n",
              "...                  ...\n",
              "2019-05-15 09:30:00  1.0\n",
              "2019-05-15 10:38:24  1.0\n",
              "2019-05-16 10:01:27  1.0\n",
              "2019-05-17 09:30:00  1.0\n",
              "2019-06-05 09:30:00  1.0\n",
              "\n",
              "[1088 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vRODJaP25-CK",
        "colab_type": "code",
        "outputId": "96583392-6e56-416b-b224-eb663d8535ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "#mlfinlab함수 , exponential은 없음.\n",
        "\n",
        "sample_weights.get_weights_by_time_decay(tripple_barrier_events,close,3,1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-11-24 15:09:55.117027 100.0% num_concurrent_events done after 0.02 minutes. Remaining 0.0 minutes.\n",
            "2019-11-24 15:09:55.947356 100.0% _get_average_uniqueness done after 0.01 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2009-10-02 09:32:42    1.0\n",
              "2009-10-02 10:34:31    1.0\n",
              "2009-10-05 12:55:39    1.0\n",
              "2009-10-06 09:31:29    1.0\n",
              "2009-10-06 11:16:28    1.0\n",
              "                      ... \n",
              "2019-05-15 09:30:00    1.0\n",
              "2019-05-15 10:38:24    1.0\n",
              "2019-05-16 10:01:27    1.0\n",
              "2019-05-17 09:30:00    1.0\n",
              "2019-06-05 09:30:00    1.0\n",
              "Name: tW, Length: 1088, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COslEu9I_05e",
        "colab_type": "code",
        "outputId": "d48f5345-888b-455e-da71-690b481a7b6d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "#c=1\n",
        "plt.plot(getExTimeDecay(tW,1,1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tW    1.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd5056bd7f0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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8FADgxgS17bNixQoVFBRo/fr16tOnj0pKSiR9/HS/aNEijRw5UkeOHNHq1avlcDgUHx+v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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtS-Wbg09sMH",
        "colab_type": "code",
        "outputId": "fb2c8a6c-8af4-4b49-c5c9-d605f6a008ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        }
      },
      "source": [
        "#c=0.6\n",
        "plt.plot(getExTimeDecay(tW,0.6,1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tW    0.6\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.001069\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd5055a96a0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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67P3E2HBmTRoShJ4JIfoLCf9eaMuurzlV3cyMiYOJiw4nNkpPbJSBmAEGYqP0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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJOeNAXJ_jXQ",
        "colab_type": "code",
        "outputId": "f0165d62-6761-4d24-d62d-4040c9c83a8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "#mlfinlab\n",
        "plt.plot(sample_weights.get_weights_by_time_decay(tripple_barrier_events,close,3,0.6))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-11-24 15:21:48.761523 100.0% num_concurrent_events done after 0.02 minutes. Remaining 0.0 minutes.\n",
            "2019-11-24 15:21:49.614056 100.0% _get_average_uniqueness done after 0.01 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd50557a240>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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67P3E2HBmTRoShJ4JIfoLCf9eaMuurzlV3cyMiYOJiw4nNkpPbJSBmAEGYqP0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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vV61HpSAW4d",
        "colab_type": "code",
        "outputId": "a1a4dafe-085e-4a5f-c031-c7515b965243",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "plt.plot(sample_weights.get_weights_by_time_decay(tripple_barrier_events,close,3,0)),\n",
        "plt.plot(getExTimeDecay(tW,0,1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-11-24 15:22:56.227088 33.33% num_concurrent_events done after 0.02 minutes. Remaining 0.03 minutes.\r2019-11-24 15:22:56.284989 66.67% num_concurrent_events done after 0.02 minutes. Remaining 0.01 minutes.\r2019-11-24 15:22:56.301029 100.0% num_concurrent_events done after 0.02 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "tW    0.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.002672\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "2019-11-24 15:22:57.025052 33.33% _get_average_uniqueness done after 0.01 minutes. Remaining 0.02 minutes.\r2019-11-24 15:22:57.064459 66.67% _get_average_uniqueness done after 0.01 minutes. Remaining 0.0 minutes.\r2019-11-24 15:22:57.141752 100.0% _get_average_uniqueness done after 0.01 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd5053e8668>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0iyIiYTOiAr6i/NdMpIn94z/PzHm3ON2OiEhEjZg3Wf0+HylHd3GULKYr3EVk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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J73oRALMAn0e",
        "colab_type": "code",
        "outputId": "64e5d04a-c7a0-44f4-a9e8-e992512f333b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "plt.plot(sample_weights.get_weights_by_time_decay(tripple_barrier_events,close,3,-0.4)),\n",
        "plt.plot(getExTimeDecay(tW,-0.4,1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-11-24 15:23:25.794530 100.0% num_concurrent_events done after 0.02 minutes. Remaining 0.0 minutes.\n",
            "2019-11-24 15:23:26.665069 100.0% _get_average_uniqueness done after 0.01 minutes. Remaining 0.0 minutes.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "tW   -0.666667\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.004454\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd5053f99b0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xx9y5c9myZQt33HFH5LoHgn19HHjvfzFDwdOOOZieQndPX0T7OJPatGR6wlg/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\nSgEvIhKn/j8hlM4B4CXe+AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2a2lZReA3G2",
        "colab_type": "code",
        "outputId": "5ed2b44d-5184-4e6f-fb1e-c3c10b0d1b89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        }
      },
      "source": [
        "plt.plot(getExTimeDecay(tW,-0.75,1))\n",
        "plt.plot(getExTimeDecay(tW,-0.25,1))\n",
        "plt.plot(getExTimeDecay(tW,0,1))\n",
        "plt.plot(getExTimeDecay(tW,0.25,1))\n",
        "plt.plot(getExTimeDecay(tW,0.75,1))\n",
        "plt.plot(getExTimeDecay(tW,1,1))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tW   -3.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.010689\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n",
            "tW   -0.333333\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.003563\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n",
            "tW    0.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.002672\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n",
            "tW    0.25\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.002004\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n",
            "tW    0.75\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.000668\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n",
            "tW    1.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64 tW    0.0\n",
            "Name: 2019-06-05 09:30:00, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fd50527ba90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lRawfALVAht+YTMJxE8N0cjI88MlIgZdMOkIInGgUq7eXvlaLcHMndjRCsrWV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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2KOGNuCIAvBS",
        "colab_type": "text"
      },
      "source": [
        "# 4.5 \n",
        "### label 0이 2/3 1이 1/3이라고 하자."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3BoRQyRCEkJG",
        "colab_type": "text"
      },
      "source": [
        "## (a) without balancing class weight"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCPg20h9E53r",
        "colab_type": "text"
      },
      "source": [
        "The classifier will maximize accuracy by **over-predicting** the dominant class\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRK_DLtZEz6l",
        "colab_type": "text"
      },
      "source": [
        "## (b)\n",
        "A label 1 means a true positive, and a label 0 means a false positive. By\n",
        "applying balanced class weights, we are forcing the classifier to pay more\n",
        "attention to the true positives, and less attention to the false positives. Why\n",
        "does that make sense?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ba51Ins1FUQJ",
        "colab_type": "text"
      },
      "source": [
        "Tying the output to real-life purpose means that too many false positives result in bad trades/investments which means lost capital. From a ML perspective without balanced class weights we will maximize accuracy by simply predicting the dominant class. **We need to improve precision: TP/(TP+FP) relative to recall: TP/(TP+FN)** not just overall accuracy (TP+TN)/(TP+FP+TN+FN)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgVl8sAMF46o",
        "colab_type": "text"
      },
      "source": [
        "### (c)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnd5w6tIEziS",
        "colab_type": "text"
      },
      "source": [
        "would be more evenly distributed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWk-Ut0kGCX_",
        "colab_type": "text"
      },
      "source": [
        "# 4.6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beGNUd_QGEEH",
        "colab_type": "text"
      },
      "source": [
        "# 4.7"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6yTIXEMF-36c",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}